Virtual mask alignment for fit analysis

ABSTRACT

Apparatus and associated methods relate to fitting a virtual mask to a virtual face by first fitting a chin region of the virtual mask to the virtual face, then determining an virtual mask angle that maintains the fitted chin region while simultaneously fitting a nose-bridge region of the virtual mask to the virtual face, and then calculating a fit-quality metric corresponding to the fitted position. In an illustrative embodiment, the fitted chin region may include the high curvature menton region of the chin. In some examples, a virtual mask may be virtually pressed toward the virtual face using a predetermined force corresponding to a force of a mask securing device of a real mask corresponding to the virtual mask In an exemplary embodiment, the fitting of a virtual mask to a virtual face may advantageously yield a mask&#39;s fit quality in a brief amount of time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and fully incorporates thedisclosures of the following previously submitted applications byreference herein:

13/839,056 System and Method for Selecting a Mar. 15, 2013 Respirator13/839,186 System and Method for Selecting a Mar. 15, 2013 Respirator61/814,897 System and Method for Selecting PPE Apr. 23, 2013 61/814,905System and Method for Evaluating PPE Apr. 23, 2013 Fit 61/861,294Virtual Mask Fitting System Aug. 1, 2013 61/917,171 Virtual MaskAlignment for Fit Analysis Dec. 17, 2013

TECHNICAL FIELD

Various embodiments relate generally to personal protective equipmentand more specifically to a system and method for predicting an optimalfit of a respirator to a facial area.

BACKGROUND

Personal Protective Equipment (PPE), such as for example respirators,are widely used in a variety of different applications. For example,many workplaces that subject an employee to hazardous atmosphericconditions require the employee to wear respiratory protection forseveral hours per day. To be effective, respiratory protection requiresa proper seal upon a facial area of the user. A poor seal and thus poorfit may result in leakage and the possibility of the inhalation ofcontaminants.

Finding a respirator that fits a unique facial area of the user canrequire the user to try on many different types and sizes ofrespirators. In some workplace environments, valuable time can be spentattempting to find an optimal fitting respirator. In other workplaceenvironments, an employee may not be able to find a respirator having asuitable fit. For example, the employee may not be given adequate timeto try on different respirators or the employee may not be given anadequate variety of respirator samples to try.

SUMMARY

Apparatus and associated methods relate to fitting a virtual mask to avirtual face by first fitting a chin region of the virtual mask to thevirtual face, then determining a virtual mask angle that maintains thefitted chin region while simultaneously fitting a nose-bridge region ofthe virtual mask to the virtual face, and then calculating a fit-qualitymetric corresponding to the fitted position. In an illustrativeembodiment, the fitted chin region may include the high curvature mentonregion of the chin. In some examples, a virtual mask may be virtuallypressed toward the virtual face using a predetermined forcecorresponding to a force of a mask securing device of a real maskcorresponding to the virtual mask In an exemplary embodiment, thefitting of a virtual mask to a virtual face may advantageously yield amask's fit quality in a brief amount of time.

When an activity requires a person to wear a mask to protect the personfrom a known hazard, one or more fit tests may be performed to ensurethat the mask seals properly to a person's face. Fit tests may be timeconsuming, as the person first may need to select a mask for testing,and then don the selected mask. After donning the selected mask, theperson may subject him/herself to a qualitative test. The person maythen stand in a testing chamber in which the ambient may be exposed tochemicals that the user can detect by smell or taste, for example. Ifthe user detects the chemical introduced into the ambient, the mask sealmay be determined to be inadequate. Such a qualitative test may taketens of minutes to complete. And the results of the test are not preciseas to the quality of the fit. For example, one may not be able todetermine if the mask fit has a small seal leak or a large seal leak.

Sometimes the person may then undergo a quantitative test. In this test,the mask wearing person may have a machine connected to a mask portalvia a tube. The machine may then monitor the quality of the exhalationsfrom the mask portal. Measurable chemicals may be introduced into thetesting chamber. If the chemicals are detected in the exhalationchemistry, the machine may measure the concentration of the detectedchemical. The machine may then determine a magnitude of the mask sealleak. This test may take additional tens of minutes to perform. Afterperforming the above described tests, the person often may have torepeat the testing process wearing another mask selected for testing.Such repetitions can be very time consuming and/or expensive.

Various embodiments may achieve one or more advantages. For example,some embodiments may facilitate the virtual fitting of many masks to auser in a brief amount of time. In some embodiments, the time and costof performing qualitative and quantitative testing of masks that areunlikely to fit well may be eliminated. In some embodiments, an abilityto suggest a mask having a good likelihood to provide a proper seal mayresult in more comfortable mask assignments. Such comfort may translateinto improved worker productivity and/or increased mask use. In someembodiments, a database of users' 3D virtual faces may be used to directinventory decisions. In an exemplary embodiment, the database of 3Dvirtual faces may direct future mask development activities. Safetymasks having improved fit for a variety of faces may result from usingsuch a database.

Apparatus and associated methods may relate to determining a fit-qualitymetric for a mask/face combination based upon a calculated dead-spacevolume between a virtual mask and a virtual face virtually aligned so asto create an integrity seal circumscribing a mouth and nose region. Inan illustrative embodiment, an interactive virtual fitting system mayreceive a three-dimensional (3D) virtual face associated with a person.The system may retrieve 3D models of various respirators selected byuser determined criteria. The system may then compute a fit-qualitymetric for each of the retrieved 3D models. The potential wearer maythen be presented with the metrics for review. The potential wearer mayselect a respirator based upon these computed metrics. A virtual fittingof many respirators may advantageously reduce the time needed forselecting a properly fitting respirator while simultaneously ensuringthat the selected respirator may be comfortable and well fitting.

Some embodiments may reduce the time needed for a person to be fit to aPPE device. In some embodiments, the person to be fit may need not bepresent at the location where the virtual fitting may be computed. Theperson to be fit, may need not be present at a storage facility for PPEdevices. Sample fitting devices need not be purchased. The eliminationof sample fitting devices may preclude the need for cleaning suchdevices between fittings. Inventories of PPE devices may be reduced bythe elimination of sample fitting devices.

Potential wearer's of PPE devices may have more devices virtually fitthan they would physically try on. This increased fitting count maytranslate into improved matching of PPE device to a wearer. Wearer's mayfind more comfort in their selected PPE devices. And PPE devices mayproperly fit a wearer. This proper fit may translate into improvedprotection against harm.

The details of various embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary PPE virtual fitting station.

FIG. 2 depicts an exemplary mask face alignment along the mid-sagittalplane cross-section.

FIG. 3 depicts a side view of a 3D captured human face and thedetermination of facial profile along the mid-sagittal plane.

FIG. 4 depicts an exemplary breathing mask cross-sectioned along themid-sagittal plane.

FIG. 5 depicts the surfaces of an exemplary breathing mask along themid-sagittal plane of the mask.

FIGS. 6A-6C depict the facial contacting menton region of an exemplarybreathing mask and the corresponding menton region of a virtual facealong the mid-sagittal plane.

FIG. 7 depicts the juxtaposition of a virtual mask on a virtual face,aligned to the best fit of the menton region.

FIG. 8 depicts a schematic of a virtual mask being rotated into anoptimal fit position on a user's virtual face.

FIG. 9 depicts an exemplary breathing mask with exemplary landmarkpoints identified around a sealing periphery.

FIG. 10 depicts an exemplary method for predicting the fit of a virtualmask to a virtual face.

FIG. 11 depicts an exemplary method for aligning a menton region of avirtual mask to a menton region of a virtual face.

FIG. 12 depicts an exemplary method for determining a rotation anglebetween a virtual mask to a virtual face.

FIG. 13 depicts an exemplary method for pressing a virtual mask into avirtual face.

FIG. 14 depicts a block diagram of an exemplary virtual fitting system.

FIG. 15 depicts an exemplary GUI screenshot during a three-dimensionalfacial acquisition.

FIG. 16 depicts an exemplary depiction of the facial model compared witha user's face.

FIG. 17 depicts an exemplary GUI screenshot of a PPE device presentationto a user.

FIG. 18 depicts an exemplary GUI screenshot of the presentation of afitting metric resulting from the computation of a virtual fit.

FIG. 19 depicts an exemplary GUI screenshot of the presentation of afacial sealing location of a PPE device as computed during a virtualfit.

FIG. 20 depicts an exemplary GUI screenshot of a sealing metric of a PPEdevice as computed during a virtual fit.

FIG. 21 depicts an exemplary GUI screenshot of a dead-space metric of aPPE device as computed during a virtual fit.

FIG. 22 depicts a block diagram of an exemplary virtual fitting system.

FIG. 23 depicts a flow chart of an exemplary virtual fitting system.

FIG. 24 depicts an overview of an exemplary respirator selection system.

FIG. 25 depicts a flowchart of an exemplary PPE selection system.

FIG. 26 depicts an overview of an exemplary PPE selection system.

FIG. 27 depicts a flowchart of an exemplary process of a static modelingmodule.

FIG. 28A depicts a flowchart of an exemplary dynamic modeling moduleusing actual body movement.

FIG. 28B depicts an exemplary graphical view of the dynamic modelingmodule of FIG. 28A.

FIG. 29A depicts a flowchart of an exemplary dynamic modeling moduleusing simulated body movement.

FIG. 29B depicts a graphical view of the exemplary dynamic modelingmodule of FIG. 29A.

FIG. 30 depicts an overview of another exemplary PPE selection system.

FIG. 31 depicts a flowchart of an exemplary PPE selection system.

FIG. 32A depicts a flowchart of another exemplary PPE selection system.

FIG. 32B depicts an exemplary center part on a ROI as defined withreference to FIG. 32A.

FIG. 33 depicts a flowchart of an exemplary deformation process.

FIG. 34 depicts a graphical representation of an exemplary color-codeddisplay of a PPE fit.

FIG. 35 depicts a flowchart of an exemplary color-coded resultgenerator.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

To aid understanding, this document is organized as follows. First, anexemplary method of virtually aligning a virtual mask to a virtual facewill be described with reference to FIGS. 1-13. Then, with reference toFIGS. 14-22, an exemplary virtual mask-fitting system that implementssome of the virtual alignment techniques described in FIGS. 1-13. Thediscussion of alignment techniques begins with a description of anexemplary virtual alignment system, with reference to FIGS. 1 and 10.Then an exemplary chin region alignment technique will be described withreference to FIGS. 2-6C and 11. This will be followed by a descriptionof an exemplary nose-bridge fitting technique, with reference to FIGS.7-8 and 12. An exemplary virtual mask-tightening procedure will bedescribed with reference to FIGS. 9 and 13. The discussion of a virtualmask-fitting system begins, with reference to FIGS. 14-21, by describingan exemplary PPE selection by a user using a virtual fitting station.Then, with reference to FIG. 22, a description of exemplary systemcomponents for a virtual fitting station will be detailed. Then, withreference to FIG. 23, an exemplary method of PPE selection using avirtual fitting station will be described.

FIG. 1 depicts an exemplary PPE virtual fitting station. In the FIG. 1depiction, a user 100 is seated at a virtual fitting station 110. Thevirtual fitting station includes a 3D facial scanner 105, a displaydevice 140 and a mask fitting engine 170. The mask fitting engine 170 isdepicted having a processor 165 which may execute instructions receivedfrom program memory locations 175. The received program instructions maycause the processor 165 to perform operations related to obtaining datarepresentative of a 3D facial model representative of a user's face,aligning the facial model to a 3D mask model, and calculating afit-quality metric characterizing a quality of the mask model fit to thefacial model. Such processor operations and/or other operations berepeated on other 3D mask models representative of different modelsand/or makes of masks. Such processor operations and/or other operationsmay be repeated on other facial models representative of other users'faces. These operations may advantageously return fit-quality metricsfor one or more user/mask combinations in a short time. Thesefit-quality metrics may select real mask models, which may be testedfurther for fit integrity. Such further fit integrity testing may beavoided for user/mask combinations associated with a poor virtualfit-quality metric.

In the FIG. 1 depiction, a user 100 is operating a 3D facial scanner 105at a virtual fitting station 110. The 3D facial scanner 105 has scanneda user's face 115 creating facial elevation data 120 and correspondingfacial position data 125. A virtual fitting engine 130 may create avirtual face 135 based upon the scanned facial elevation data 120 andthe corresponding facial position data 125. The virtual fitting engine130 may send image data corresponding to the virtual face 135 to adisplay device 140. The virtual fitting engine 130 may retrieve avirtual mask 145 from a mask database 150. The virtual mask 145 mayinclude a virtual facial-contacting surface 155 configured to contact avirtual face 135 circumscribing a nose and mouth region of a virtualface 135. The virtual fitting engine 130 may virtually align the virtualmask 145 to the virtual face 135 near a high curvature location of achin region. The virtual fitting engine 130 may virtually rotate thevirtual mask 145 about a chin region rotation point to bring anose-bride region of the virtual mask 145 into virtual proximity with anose-bridge region of the virtual face 135. In some embodiments, thevirtual mask 145 may be virtually pressed against the virtual face 135to simulate a force of a real mask attachment device when securing areal mask to a real face. The virtual fitting engine 130 may thencalculate a fit-quality metric 160 based upon a relationship of thevirtual facial-contacting surface 150 to adjacent facial elevation data120 when the virtual mask 145 has been aligned as described above. Insome embodiments, the fit-quality metric 160 may be calculated byevaluating the proximity of a virtual facial-contacting surface 155along a path circumscribing the nose and mouth region of the virtualface 135. The calculated fit-metric 160 may be associated with amask-user combination. The virtual fitting engine 130 may send a signalrepresentative of the fit-quality metric 160 for display to the displaydevice 140. After an initial scan of the user's face 115, many differentvirtual masks may be virtually fit to the virtual face 135 associatedwith the user 100 perhaps without need of the users continuedparticipation. In this way, one or more well-fitting masks may beoptimally selected from the mask database 150 for trial by the user 100.

FIG. 2 depicts an exemplary mask/face alignment along a mid-sagittalplane cross-section. In the FIG. 2 depiction, an exemplary virtual mask200 and an exemplary virtual face 205 are shown in cross-section along amid-sagittal plane (the plane of the drawing sheet). A menton region 210and a sellion region 215 of the virtual mask are identified. The virtualface 205 also has a menton region 220 and a sellion region 225. Acoordinate system depicted in the figure aligns a Y-axis 220 in thevertical direction and a Z-axis 225 in the horizontal direction withinthe mid-sagittal plane. An X-axis (not depicted) may extendperpendicular to the drawing sheet. The virtual mask 200 may be fit tothe virtual face 205 first at the menton region 210. The menton region220 of the virtual face 205 may have high curvature in the Y-Z plane ata forward chin location. The virtual mask 200 may have a complementaryhigh-curvature in the menton region 210 for receiving the high-curvatureof the forward chin location. This high-curvature region may provide aninitial fitting relationship between the virtual mask 200 and thevirtual face 205. Then the virtual mask 200 may then be rotated aboutthe fitted menton region 210 to optimally fit the sellion region 215 ofthe virtual mask 200 to the sellion region 225 of the virtual face 205.In some embodiments, the sellion region 215 may first be fit and thenthe mask rotated to fit the menton region 210. By fitting the mask alongthe mid-sagittal plane, a small fraction of the virtual contact pointsof the mask/face interface may be considered. This, in turn may resultin an initial mask/face alignment being quickly determined.

FIG. 3 depicts a side view of a 3D captured human face and thedetermination of facial profile along the mid-sagittal plane. In theFIG. 3 depiction, a captured 3D human face 300 is displayed from a sideperspective so that a mid-sagittal plane profile 305 may be clearlydepicted. The mid-sagittal plane has a Z-axis 310 and a Y-axis 325. Insome embodiments, the maximal projection in the Z-direction 310 of theimage may be used to identify a nose tip 315 of the captured 3D humanface 300. A location of a menton region 320 may then be obtained usingthe relative Y and Z positions of the chin from the nose tip 315. Insome embodiments, the curvature of the mid-sagittal plane profile 305may be used to determine the location of the menton region 320. Locatingthe menton region 320 from the captured 3D human face 300 may provide aninitial location for fitting a virtual mask, for example. In someembodiment, the mid-sagittal plane may be determined by finding a lineof facial symmetry, for example. The determined mid-sagittal plane maybe used to center a virtual mask upon a virtual face. After centering avirtual mask upon a virtual face, a vertical (Y-axis) location may beobtained for aligning a virtual mask to a virtual face. Where a face haslittle curvature along this vertical dimension, the face may present fewlocating features. But where the face has large curvature along thevertical dimension, locating features may be presented. If a virtualmask is designed to engage such a large curvature locating feature (e.g.chin or nose), for example, locating these features may assist infinding a good initial position for mask alignment.

FIG. 4 depicts an exemplary breathing mask cross-sectioned along themid-sagittal plane. In the FIG. 4 depiction, an exemplary virtual mask400 is shown from a rear perspective view. The exemplary virtual mask400 has been cross-sectioned along the mid-sagittal plane so that amid-sagittal elevation profile 405 may be seen. A facial-contactingsurface 410 of the depicted virtual mask 400 is shown. A real contactingsurface of a corresponding real mask may be made of a soft and/ordeformable material. Such a deformable material may facilitateconformity of the mask to a user's face when the mask is pressed intothe face by a mask securing device. Such conformity with the user's facemay provide an integrity seal of the mask to the face around amouth/nose perimeter path. A facial contacting region of themid-sagittal elevation profile 405 may be used for an initial locatingof the virtual mask to the virtual face, for example. In someembodiments, the menton region of the facial contacting surface 410 maybe used to initially locate the virtual mask to the virtual face. Thefacial contacting surface 410 may present a curvature region that issubstantially complementary to a facial curvature feature. Masks may bedesigned to engage a facial feature having a large curvature. Some masksmay have malleable engagement surfaces that may deform when pressed intoa user's face. A virtual mask 400 may include indicia of malleability.These indicia of malleability may vary with position. For example. Themalleability of a facial-engagement surface 410 may be a function oflocation along the facial-engagement surface 410. This malleabilityfunction may be used in determining a pressed position of a mask againsta user's face, for example. Pressing a mask against a wearer's face mayminimize or eliminate air gaps in the perimeter of theengagement-surface/user's-face interface.

FIG. 5 depicts the surfaces of an exemplary breathing mask along themid-sagittal plane of the mask. In the FIG. 5 depiction, the virtualmask data 500 plotted in a Y-Z coordinate system. The virtual mask data500 is the mid-sagittal profile of a virtual mask. The origin of the Y-Zcoordinate system centers the mid-sagittal profile data 500 in thecoordinate system. The mid-sagittal profile 500 includes a mentoncontacting surface 505 and a nose-bridge contacting surface 510. Thementon contacting surface 505 is depicted with a curvature in the Y-Zplane. The menton contacting surface may be obtained by first extractingpoints in the third quadrant 515 of the depicted coordinate system, forexample. The mid-sagittal profile data 500 in the third quadrant 505 mayinclude both a menton contacting surface 505 and non-contacting surfaces520. This menton contacting surface 505 may be extracted by selecting asubset of the third quadrant data having the most negativeZ-coordinates, for example. Then those data that are monoticallyincreasing as their Y-coordinates increase (concave-up curvature) may beselected from this most negative Z-coordinate subset. This selected datamay be used to fit the virtual mask to a virtual face at a mentonregion, for example. In some embodiments, the virtual mask may identifythe menton contacting surface 505 as part of the model. In someexemplary embodiments, the selection of the menton contacting surfacemay need to be performed only once when a virtual mask model is beingcreated. The selection of the nose-bridge contacting surface may befound in similar manners.

FIGS. 6A-6C depict the facial contacting menton region of an exemplarybreathing mask and the corresponding menton region of a virtual facealong the mid-sagittal plane. In FIG. 6A, third quadrant menton regiondata 600 is displayed. The third quadrant menton region data 600includes a facial contacting surface 605 and non-contacting surfaces610. In FIG. 6B, the facial contacting surface 605 has been aligned to amid-sagittal facial profile 615 of a person. In some embodiments, aleast-squared fitting method may be used to determine the optimal fitlocation of the facial contacting surface 605 to the mid-sagittal facialprofile 615. In some embodiments a sliding window analysis may be usedto determine an optimal fit location. In an illustrative embodiment,interpolation between adjacent data points may be used to increase thedensity of the data points to be fit. In some embodiments, interpolationmay be used to provide a common coordinate system for data comparisonand/or fit. In an illustrative embodiment, the optimal fit may bedetermined by aligning the points of maximum curvature of the virtualmask to that of the virtual face, for example. In an exemplaryembodiment, elasticity values may be assigned to each data point of thementon region data facial contacting surface 605. The elasticity valuesmay be a function of the location of each data point, for example. Insome embodiments, the elasticity values may be used in determining theoptimal fit location. For example, a weighting factor corresponding tothe elasticity may be assigned to each data point. A highly elastic datapoint may be assigned a low weighting value. This may permit the datapoint to fit more poorly to the face than a data point with a highweighting value corresponding to a low elasticity. FIG. 6C depicts aclose-up view of the facial contacting surface 600 positioned in theoptimal fit location with respect to the mid-sagittal facial profile 615of the person.

FIG. 7 depicts the juxtaposition of a virtual mask on a virtual face,aligned to the best fit of the menton region. In FIG. 7 a virtual mask700 has been translated to a virtual face 705. The translation may havealigned a previously determined optimal fit position of the mentonregion 710 of the virtual mask 700 to that of virtual face 705. In thedepicted alignment, a sellion region 715 of the virtual mask 700 may bepoorly aligned relative to a sellion region 715 of the virtual face 705.The virtual mask 705 may be rotated such that the sellion region 715 ofthe virtual mask 700 is pressed into the virtual face 705. Such arotational position would likely be uncomfortable or even impossible,even with consideration of a mask's elasticity. Thus the virtual mask700 may need rotation away from the sellion region 715 of the virtualface 705 to obtain a better quality fit. In some embodiments the virtualface 705 may be rotated to accommodate any rotational mismatch between avirtual mask 700 and a virtual face 705.

FIG. 8 depicts a schematic of a virtual mask being rotated into anoptimal fit position on a user's virtual face. In the FIG. 8 depiction,a user's virtual face 800 has a virtual mask 805 superimposed onto it atan initial alignment position. A chin region 810 has been fit, but anose-bridge region 815 requires fit modification. The angle between thevirtual mask 805 and the virtual face 800 may require modification. Adetermination of an optimal angle may include a determination of anoptimal nose-bridge mask/face alignment, for example. A point ofrotation may be selected near the chin region 810, for example. Thepoint of rotation may be selected at the central point of a mentoncontacting surface, for example. In some embodiments, a point near aforward chin-bone location of the virtual face 800 may be selected asthe point of rotation. In some embodiments, a least squares rotationoperation may be performed. In an exemplary embodiment the optimumrotation may be performed using weighted data corresponding toelasticities of the mask at various locations along a facial-contactingsurface. In some embodiments, a fit metric corresponding to each datapoint may be based upon the difference in a Z-direction between thevirtual mask 800 and the virtual face 805. In some embodiments, a fitmetric corresponding to each data point may be based upon the distancebetween the virtual mask 800 and the virtual face 805 normal to asurface of the virtual mask 800 and/or the virtual face 805.

FIG. 9 depicts an exemplary breathing mask with exemplary landmarkpoints identified around a sealing periphery. In the FIG. 9 embodiment,an exemplary virtual mask 900 has a contacting surface 905 configured toperipherally seal the virtual mask 900 to a virtual face around a noseand mouth region. After fitting a virtual mask 900 to a virtual face,some embodiments press the virtual mask 900 into the virtual face. Thispressing operation may simulate a real force imparted by a real maskonto a real face by a real mask securing device. In some embodiments, anelastic band may serve as a mask securing device. In some embodiments atightening strap may serve as a mask securing device. A real masksecuring device may force a mask into a face to provide an integrityseal between the mask and the face of a wearer. In an exemplaryembodiment, a functional relationship may be defined between adeformability parameter and a location of the contacting surface 905.For example, each location of the contacting surface may have acorresponding deformability value. The virtual mask 900 may betranslated in the direction of a virtual face in response to a virtualforce. The translation may proceed until the virtual force required forsuch a translation exceeds a predetermined threshold, for example. Insome embodiments, the virtual force may be calculated by integrating theforce required for deformation around the periphery of the contactingsurface 905 of the virtual mask 900. For example, the initial positionof the mask may contact the user at three distinct points before thetranslation begins. The mask may be incrementally translated in thefacial direction. After each translation, an integrated force may becalculated around the sealing periphery. The translation may beterminated when the calculated force exceeds a predetermined threshold.In some embodiments the translation may be terminated when a maximumforce at any discrete location exceeds a predetermined threshold. Insome embodiments, the mask may be further rotated after a force has beencalculated. For example, an integrated force on an upper region of themask may be much greater than an integrated force on a lower region ofthe mask. To balance the upper and the lower force, the mask may berotated, for example. Further translation may again be resumed after themask forces have been equilibrated, for example.

In some embodiments, an integration may be approximated by a summationof discrete landmark points around a mask periphery. Many landmarkpoints may be evaluated, in some embodiments. For example, landmarkpoints may be selected around a sealing surface that maycircumferentially seal around a user's mouth and nose. Each landmarkpoint may be assigned a toleration value, a component of which mayrepresent a flexion value of the mask at that landmark point location,and a component of which may represent a reasonable tolerance of tissuedeformation at a facial position associated with the landmark pointlocation. Various numerical means of determining an optimal rotation maybe used. For example, such techniques as least-squared fit methods maybe used in determining the optimal rotation of the virtual mask. In someexamples, weighted regression techniques may be used to determine theoptimal rotation.

FIG. 10 depicts an exemplary method for predicting the fit of a virtualmask to a virtual face. In FIG. 10 an exemplary virtual fitting method1000 is described from a vantage point of the processor 165 depicted inFIG. 1. The virtual fitting method 1000 begins with the processor 165receiving a 3D virtual face of a person 1005. The 3D virtual face may beobtained by a 3D scanner 105, for example, and then communicated to theprocessor 165. In some embodiments, the 3D virtual face may have beenpreviously obtained and retrieved by the processor 165 from a storagedevice. The processor 165 then retrieves a virtual mask from a databaseof virtual masks 1010. Then the processor 165 determines an optimal chinalignment between the virtual mask and the virtual face 1015. Theprocessor 165 then determines an optimal nose-bridge fit by rotating thevirtual mask toward or away from the virtual face about the optimal chinlocation 1020. The processor 165 then translates the virtual mask, asaligned at the chin and rotated to the nose-bridge, into the virtualface 1025. This translation may simulate the translation of a real maskinto a real face in response to an applied force. In some embodiments, amask securing device may produce the force that secures a mask to aface, for example. The processor 165 may then calculate a fit-qualitymetric and associate the fit-quality metric with the virtual face-maskcombination 1030. The processor 165 may test whether this fit-qualitymetric is the best one associated with the virtual face 1035. If thefit-quality metric is better than the previous best fit-quality metricassociated with the virtual face, the fit-quality metric is assigned asthe best fit-quality metric 1040. If, however, the fit-quality metric isnot the best fit-quality metric associated with the virtual face, theprevious best fit-quality metric remains. Regardless of the outcome ofthe best fit-quality metric test, the processor then determines whetherall of the virtual masks have been evaluated for this virtual face 1045.If not, the processor returns to step 1010. If, however, all of thevirtual masks have been evaluated for this virtual face, then the listof fit metrics is displayed in descending order of fit quality and themethod ends 1050.

FIG. 11 depicts an exemplary method for identifying a menton region of avirtual face. In FIG. 11, an exemplary menton locating method 1100 isdescribed from a vantage point of the processor 165 depicted in FIG. 1.The exemplary menton locating method 1100 may be performed as part ofthe fit menton region step 1015 of the fit prediction method 1000, forexample. The menton locating method 1100 begins by identifying facialfeatures in a virtual face 1105. For example, eyes, nose and a mouth maybe identified by characteristics that are unique to each of thesefeatures. A nose-tip, for example, may be located by locating thehighest Z-elevation location in the virtual face. Then the processor 165calculates a mid-sagittal plane of symmetry 1110. The processor may, forexample, convolve a mirror image of the virtual face with thenon-mirrored virtual face. The translated location where the mirroredimage best matches the non-mirrored image may then be used to find aline of symmetry. A line of symmetry may be determined by finding theimage locations where the mirrored image aligns with the non-mirroredimage. Then the processor 165 initializes an index N 1115. The index maycorrespond to starting location on the line of symmetry from which theprocessor will begin its search for the menton region of the virtualface. The starting location may, for example, begin just beneath thelocation of the nose-tip. The processor then retrieves the first threefacial elevation points, Z_(N−1), Z_(N) and Z_(N+1), along the line ofsymmetry going from the starting point down toward the lower face 1020.A Y-Z curvature value may be calculated from these the three facialelevation points. For example, the following equation may be used as arelative curvature metric:

C=−Z _(N−1)+2Z _(N) −Z _(N+1)

Here, C is a measure of the curvature. The processor then compares thecalculated value of curvature with zero 1125. If the calculatedcurvature is less than zero, then the

FIG. 12 depicts an exemplary method for identifying a menton region of avirtual face. In FIG. 12, an exemplary angular alignment method 1200 isdescribed from a vantage point of the processor 165 depicted in FIG. 1.The exemplary angular alignment method 1200 may be performed as part ofthe rotate mask step 1020 of the fit prediction method 1000, forexample. The angular mask alignment method 1200 begins by the processor165 selecting a pivot point about which the mask may be rotated 1205.The location of the pivot point may be selected to minimize the changein the fit quality of the menton region, for example. In someembodiments, the pivot point may be located substantially at a centrallocation of the fit menton region. The processor 165 then receivesmid-sagittal facial nose-bridge elevation data of a virtual face 1210.The processor 165 then retrieves mid-sagittal mask nose-bridge elevationdata of a virtual mask 1215. The processor 165 then calculates arotational angle for aligning the received facial nose-bridge elevationdata to the retrieved nose-bridge mask elevation data 1220. Theprocessor 165 then virtually rotates the virtual mask. For example, theprocessor 165 may calculate new coordinates for each of the virtual masksurfaces based upon the calculated rotational angle for aligning thenose-bridge regions of the virtual mask and the virtual face. These newcoordinates may include those for the menton region, in some examples.The processor 165 then determines if the menton region requires beingrefit 1230. For example, the processor 165 may assess whether therotated facial contacting surface of the virtual mask remainswell-fitted to the menton region of the virtual face after rotation. Ifthe processor 165 determines that refitting the menton region isrequired, the processor 165 will again fit the menton region of therotated virtual mask to the menton region of the virtual face 1235. Themethod will then return to step 1205 and repeat the rotation of the maskby again selecting a pivot point. If, however, at step 1230, theprocessor determines that the menton region does not require refitting,the method ends.

FIG. 13 depicts an exemplary method for tightening a virtual mask. InFIG. 13, an exemplary mask tightening method 1300 is described from avantage point of the processor 165 depicted in FIG. 1. The exemplarymask tightening method 1300 may be performed as part of the translatemask step 1025 of the fit prediction method 1000, for example. The masktightening method 1300 begins with the processor 165 retrievingelasticity data associated with a facial-contacting surface of a virtualmask 1305. In some embodiments, the elasticity data may correspond to aphysical parameter of the mask. In some examples, the elasticity datamay be a function of the facial-contacting surface location. In anexemplary embodiment, the elasticity data may correspond to a facialskin thickness that varies as a function of facial location. In anexemplary embodiment, the elasticity data represents a combination ofphysical mask materials and physiological facial statistics. Theprocessor 165 then calculates an integrated force along the sealinginterface between the virtual mask and the virtual face 1310. Forexample, the processor 165 may calculate an integrated force at the maskposition aligned at a menton region and rotated to align a nose-bridgeregion. The processor 165 then determines if the calculated forceexceeds a predetermined threshold 1315. If the forced does exceed thepredetermined threshold, then the processor translates the virtual masktoward the virtual face by an incremental amount 1320. The direction oftranslation may be in a fixed Z direction, for example. In someembodiments, the direction of translation may be in a substantiallynormal direction to the general plane of the sealing interface. In someembodiments, the direction of translation may be determined by therelative forces calculated around the periphery of the sealinginterface. The processor then returns to step 1310 and recalculates theintegrated force in the translated mask position. If, however, at step1315, the calculated force exceeds the predetermined threshold, then themethod ends.

FIG. 14 depicts a block diagram of an exemplary virtual fitting system.In the FIG. 14 depiction, an exemplary virtual fitting system 1400includes a control system 1405 and a 3D facial scanner 1410. The controlsystem 1405 is in communication with a virtual fitting engine 1415 andan inventory module 1420. The control system 1405 may be running a GUIprogram retrieved from program memory 1425. The GUI program may containinstructions that, when executed, may coordinate the acquisition of a 3Dface, the virtual fitting of a 3D mask to the 3D face, ordering masksfrom venders, as well as many other PPE related activities, for example.The fitting engine 1415 may perform various alignment operations toalign a virtual mask to a virtual face. For example, the fitting engine1415 may have a rotation module 1425. In some embodiments, the fittingengine 1415 may have a translation module. Some embodiments may haverotation and/or translation modules that operate in more than onedimension. For example, a rotation may be performed about an X-axis, aY-axis or a Z-axis, or a combination thereof. The fitting engine 1415may keep statistics for use in future product development activities.The inventory module 1420 may have databases of masks 1430, users 1435,and/or venders 1440, for example. The inventory module 1420 may generateorders using an ordering engine 1445.

FIG. 15 depicts an exemplary GUI screenshot during a three-dimensionalfacial acquisition. In the FIG. 2 exemplary GUI screenshot 1500, analignment cross-hair 1505 is superimposed upon a user's real time image1510. Instructions 1515 are presented to the user on the left-hand sideof the screen. The user may be instructed to center the user's face uponthe cross-hair and to push a “confirm” button 1520 when ready. Theexemplary virtual fitting station may then perform a 3D scan of theuser's face 1510. In some embodiments, the user may be instructed tochange positions for an additional 3D facial scan. For example, the usermay be asked to open the user's mouth. The system may use thisinformation to determine the quality of fit, when the user may betalking, for example. In some embodiments a user's actual body movementmay be used to generate a dynamic facial model. Exemplary dynamicmodeling systems are described, for example, with reference to at leastFIG. 3 in U.S. patent application Ser. No. 13/839,056, entitled “Systemand Method for Selecting a Respirator,” filed on Mar. 15, 2013, theentire disclosure of which is incorporated herein by reference.

FIG. 16 depicts an exemplary depiction of the facial model compared witha user's face. In this figure, an exemplary GUI screenshot 1600 isdepicted comparing the computer facial model 1605 of two users to anexemplary virtual fitting system. The screenshot 1600 depicts a captureimage 1610 of each user's face and depicts the captured image 1610alongside the facial model 1605. The system may also compute an errorassessment image 1615. The error assessment image 1615 may show theuncertainty of the model as a function of facial position. In someembodiments, the virtual fitting system may permit the user to rejectthe model and return to a 3D facial scanning stage depicted in FIGS.15-15. A new 3D facial model may be acquired by a 3D facial scanner, forexample.

The user may then be queried as to the type of PPE device the userwishes to fit. The virtual fitting system may then retrieve, from apreviously created database, all PPE devices that match the user'scriteria. In some embodiments, the virtual fitting station may proceedto computationally determine fitting metrics for each of the matched PPEdevices. The station may then sort the matched PPE devices according thecomputed fitting metrics. For example, the virtual fitting station maypresent to the user, a screen display depicting the matched PPE devicehaving the best fitting metrics.

FIG. 17 depicts an exemplary GUI screenshot of a PPE device presentationto a user. In this figure, an exemplary screenshot 1700 depicting a PPEdevice 1705 along with certain product information 1710. In someembodiments, a virtual fitting device may first ask the user to selectfrom among the matched fitting devices before computing fitting metricson that device. In some embodiments, a virtual fitting system maypresent a screenshot containing all the matched devices along with thecomputed fitting metrics associated with each device. In someembodiments, the screenshot may present all matching devices sorted indecreasing order of a computed fitting metric.

FIG. 18 depicts an exemplary GUI screenshot of the presentation of afitting metric resulting from the computation of a virtual fit. Thisfigure depicts an exemplary GUI screenshot 1800 displaying arepresentation of the facial model 1805 wearing a selected PPE device1810. The screenshot 1800 also depicts a graphic 1815 demonstration oneof the computed fitting metrics. In this figure, the fitting metricdisplayed is the distance of the PPE device from the face, when worn. Insome embodiments, the metric displayed may be user selected via a radiobutton, for example. In some embodiments, the metric displayed may beuser selectable via a drop-down menu. In some embodiments, the user mayselect a different PPE device from a drop-down menu 1820 on this displayscreen 1800. In some embodiments, the user may change the vantage pointof the display metric. For example, the user may want to see the fittingmetric display from a close-up perspective. Or perhaps the user may wantto see the chin fit of the PPE device, and therefore may rotate thedisplay so as to better see the chin. In some embodiments, the user maybe able to control the coloration of the displayed metric. For example,the use may change the distance range of coloration from zero inches to3 inches. A user may want to color only those regions that the PPEdevice fits between one-quarter of an inch to one-half of an inch, forexample. Exemplary methods of computing fitting metrics are described,for example, with reference to at least FIG. 3 in U.S. patentapplication Ser. No. 61/814,897, entitled “System and Method forSelecting PPE,” filed on Apr. 23, 2013, the entire disclosure of whichis incorporated herein by reference.

FIG. 19 depicts an exemplary GUI screenshot of the presentation of afacial sealing location of a PPE device as computed during a virtualfit. The exemplary FIG. 19 screenshot 600 depicts another exemplaryfitting metric. In this figure, the contact line 605 between the PPEdevice and the user's face is shown. In some embodiments, the virtualfitting station may depict a graphic depicting a predicted comfort levelfor each contacted location of a user's face, for example. In someembodiments, a virtual fitting station may compute the expected level ofheat a user might expect to experience wearing a particular PPE device.In one exemplary embodiment, a virtual fitting station may compute aseal quality for a virtually-fit PPE device. For example, a virtualfitting station may present a graphic in which the contact line iscolor-coded indicating the quality of fit at the various contactlocations. Exemplary methods of computing a PPE-facial seal metric aredescribed, for example, with reference to at least FIG. 5 in U.S. patentapplication 61/814,905 titled “System and Method for Selecting PPE Fit,”filed on Apr. 23, 2013, the entire disclosure of which is hereinincorporated by reference.

FIG. 20 depicts an exemplary GUI screenshot of a sealing metric of a PPEdevice as computed during a virtual fit. In this figure, an exemplaryGUI screenshot 2000 shows a user virtually wearing a PPE device 2005.The screenshot 2000 also depicts a computed fitting metric, in thisexample, the quality of a PPE-facial seal 2010. The quality of thePPE-facial seal 2000 may be color coded, as depicted here. The qualityof the PPE-facial seal may be indicated by a single numerical metric2012, for example. The screenshot may permit the user to select from alist of PPE devices via a graphical selection area 715. These PPEdevices may be have been selected by the virtual fitting station basedupon matching criteria. The matching criteria may have been supplied bythe user. The matching criteria may have been predetermined by theuser's employer, for example. The user may be able to select a differentsize of the PPE device by selecting among a series of size buttons 2025.In some embodiments, the user may be able to navigate forward andbackwards through the various user screens using navigation buttons, forexample.

FIG. 21 depicts an exemplary GUI screenshot of a dead-space metric of aPPE device as computed during a virtual fit. In this figure, ascreenshot 2100 includes an image of a user wearing a PPE device 2105,and a graphic 2110 displaying a computed fitting metric. In thisexemplary figure, the displayed fitting metric is a dead-space score2115. The dead-space score may be displayed both graphically 2115 andusing a single numeric metric 2120 as depicted here. In someembodiments, only the graphical display may be presented. In someembodiments, only a numeric metric will be presented. In someembodiments, more than one metric may simultaneously be presented to theuser via a display screen. In some embodiments, if too little dead-spaceresults from a virtual PPE-facial fit, the dead-space score may be low.But in some embodiments, if too much dead-space results from a virtualPPE-facial fit, the dead-space score may also be low. For some PPEdevices, too much dead-space may not be considered problematic, forexample. But for other PPE device, too much dead-space may facilitatemask collapse during inhalation, for example. Exemplary dead-spacemetric methods are described, for example, with reference to at leastFIG. 8 in U.S. patent application Ser. No. 13/839,186, entitled “Systemand Method for Selecting a Respirator,” filed on Mar. 15, 2013, theentire disclosure of which is incorporated herein by reference.

FIG. 22 depicts a block diagram of an exemplary virtual fitting system.In FIG. 22, a block diagram 2200 of an exemplary virtual PPE devicefitting includes a face capture module 2205. The face capture module maybe used to capture users' 3D topological information. The topologicalinformation may then be imported a mask fitting system 2210. The maskfitting system may have a processor that may generate a 3D facial modeland store such 3D facial models in a face model database 2215. Anemployer may keep facial models of its employees in such a database, forexample, so that employees' faces need not be reacquired. A PPE productdatabase 2220 may also be maintained by the exemplary virtual fittingstation 2200. The mask fitting system 2210 may retrieve both a facialmodel from the face model database 2215 and a PPE device model from thePPE product database 2220, for example. The mask fitting system may thenuse a fitting algorithm 2225 to compute fitting metrics for the facialmodel-PPE configuration. A fitting score 2230 may be output for a user'sreview. The mask fitting system 2210 may also compute a recommendation2225 for the employee.

FIG. 23 depicts a flow chart of an exemplary virtual fitting system.FIG. 23 depicts an exemplary virtual mask-fitting method 2300 from thevantage point of a processor in the mask fitting system 2210. Theprocessor begins by presenting to an alignment image for the user toalign his live image to an alignment mark 2305. The processor then waitsuntil the user hits a “proceed” button 2310. If the user does not pushthe proceed button the processor continues to present the alignmentscreen 2305. If the user does push the “proceed” button, the processorthen instructs the 3D scan tool to acquire the user's facial topologicalinformation 2315. The processor then creates a 3D facial model using theacquired 3D facial information 2320. The computer then evaluates whetherthe model is acceptable 2325. If the model is not acceptable, theprocessor returns to step 2305 and presents the user an alignmentscreen. If the processor determines that the model is acceptable,however, the processor then requests the PPE criteria from the user2330. The processor then waits for the user to input the PPE criteria2335. When the processor receives the user input PPE criteria, theprocessor retrieves the first matching PPE device model 2340.

The computer then fits the PPE device model to the acquired 3D facialmodel 2345. The processor computes fitting metrics during this step. Thecomputer then assesses whether any more matching PPE models have yet tobe virtually fit 2350. If more matching PPE device models have yet to bevirtually fit, the processor returns to step 1040 and retrieves the nextmatching PPE device model. If all of the matching PPE devices havealready been virtually fit, however, the processor then presents the PPEdevices to the user 2355. The PPE device devices may be presented in anorder of decreasing computed fit metric, for example. The processor thenwaits for a user to select a PPE device from the presented list 2360.When the user selects a PPE device, the processor then presents the userwith a display of the computed data for the selected PPE device alongwith an image of the user wearing the selected PPE device 2365. Theprocessor then asks the user if the user is satisfied with the selectedPPE device 2370. If the user is not satisfied with the selected PPEdevice, the processor returns to step 2355 and again presents to theuser the sorted list of all matching devices. If, however, the user issatisfied with the selected device, the processor finishes the exemplarymethod.

Although various embodiments have been described with reference to thefigures, other embodiments are possible. For example, an exemplaryvirtually PPE fitting system may be performed on the cloud as a Systemas a Service (SaaS). A virtual fitting system GUI may run remotely overthe internet, for example. In some embodiments, the GUI may operatelocally, while the processor computes the fitting metrics remotely. Insome embodiments the virtual PPE fitting system may fit a human bodypart, such as for example a hand. Some embodiments, may project theusers actual real-time face upon the computed facial model. For example,the user may be moving in real time, and the selected PPE device may besuperimposed upon the user's face creating a real-time virtual fit. TheAppendix details many various additional GUI aspects of an exemplaryvirtual fitting system.

Various embodiments may use more or less menton area elevation data fromboth the virtual masks and the virtual faces to optimally fit the mentonregion. In some embodiments, after determining the optimal fit of avirtual mask to a virtual face based upon a menton fit and a sellionrotation, a fit-quality metric may be determined using the sealingsurface periphery data. For example, the sealing surface data that maycircumscribe the mouth and nose may be used to determine a fit-qualitymetric. In some embodiments, a dead-space volume, that volume betweenthe virtual facial elevations and the virtual mask interior elevationsmay be used in determining a quality metric.

In an illustrative embodiment, a computer program product (CPP) tangiblyembodied in a computer readable medium and containing instructions that,when executed, cause a processor to perform operations to determine thefit of a virtual mask to a virtual face, the operations may includereceiving facial elevation data corresponding to a face of a person. Insome embodiments, operations may include retrieving from data memorylocations mask elevation data corresponding to a facial mating surfaceof a mask model. In an exemplary embodiment, operations may includefitting a chin position of the retrieved mask elevation data to a chinregion of the received facial elevation data. In some embodiments,operations may include rotating the retrieved mask elevation data towardor away from the received facial elevation data to a nose-bridge regionof the received facial elevation data, while maintaining the fitted chinposition. In some embodiments, operations may include calculating aquality of a fit between the retrieved mask elevation data and thereceived facial elevation data at the rotated position.

Personal Protection Equipment (PPE), such as for example respirators,are widely used by persons working in extreme environments. For example,some workplaces require employees to work in hazardous atmospheres. Ahazardous atmosphere may be one with excessive dust or particulatecontamination, for example. A hazardous atmosphere may be one withchemical vapors present. Some nuclear facilities may have radioactivegas in the working atmosphere. Hospitals may present biologicalcontaminants in the atmosphere. To provide safety for the worker,respirators may be used by the workers who are exposed to suchatmospheres. Some employers may require certain employees to wearpersonal protection equipment to protect the employees from exposure tothe hazards.

The quality of fit of personal protection equipment affects the level ofprotection that may be provided by the equipment. For example, if arespirator fits improperly, the employee wearing the respirator may beexposed to the hazard. If the quality of fit is poor, a respirator maybe uncomfortable to wear. Many different types of respirators or otherPPE devices are available in the marketplace. And each different pieceof equipment may have a different presentation to the wearer. It can bea time consuming job to evaluate the many different equipment devicesfor the purpose of finding one with a good seal, and yet still iscomfortable to wear.

Apparatus and associated methods may relate to a system for predicting arespirator fit by comparing a respirator model in a deformed state to aspecific facial model. In an illustrative example, an internalmeasurement may be calculated between an inside part of the respiratormodel and the facial model. The internal measurement may be comparedagainst a predetermined threshold to determine a fit of the respiratormodel, for example. In various implementations, the internal measurementmay be a distance and/or a volume between the respirator and facialmodel. In some implementations, a 3D representation of the respiratormodel may be displayed upon a 3D representation of the facial model. Insome implementations, a color-coded facial display may characterizeareas of comfort and discomfort with respect to the respirator model.For example, areas of comfort and discomfort may be objectivelydetermined in view of an applied pressure by the respirator.

In accordance with an exemplary embodiment, an image capture device maygenerate point cloud data of a body part model and a PPE model. Forexample, an image capture device may generate point cloud data of afacial area and a respirator. In an exemplary embodiment, point clouddata may be used to overlay a respirator model on a facial model todetermine a virtual placement of the respirator model on the facialmodel. For example, a rigid registration method may be used to alignpoint clouds of the facial model and the respirator model. In someimplementations, identifying feature points of the body part model(e.g., nose, mouth) may be correlated with the generated point cloud. Insome implementations, a contact line may be determined upon the facialarea. Determination of the contact line may provide identification of aportion of a facial area that aligns with the inside part of therespirator.

Various embodiments may achieve one or more advantages. For example,some embodiments may objectively determine whether the hidden, insidepart of the respirator will make contact with a portion of the facialarea. In accordance with an exemplary embodiment, a predetermineddeformation parameter of an outside part (e.g., outside surface) of therespirator may be attributed to a corresponding inside part (e.g.,inside surface) of the respirator. The deformation parameter may bedetermined at each of the vertices or point cloud of the outside andinside parts of the respirator. In an exemplary embodiment, internalmeasurements may be made between each deformed point cloud or each ofthe vertices and an aligned part of the facial area to determine arespirator fit.

Apparatus and associated methods may relate to a system for predicting arespirator fit by comparing a specific respirator model to a specificfacial model in a dynamic position. In an illustrative example, one ormore dynamic positions may be generated by actual user movement and/orsimulated user movement. For example, a facial model may be generated byaltering a static model in view of actual and/or simulated movements. Invarious implementations, a facial model may be compared against avariety of respirator models from a respirator model database. In someimplementations, a 3D representation of the respirator model may bedisplayed upon a 3D representation of the facial model.

Some embodiments may predict a realistic fit of a respirator to a facialarea by modeling the facial area in one or more dynamic positions. Forexample, the dynamic positions may be characteristic of facial movementsthat a user may undergo while wearing the respective PPE, such as forexample an open mouth, a raising head, or a bowing head. In an exemplaryembodiment, the dynamic positions may be extreme facial movements.

FIG. 24 depicts an overview of an exemplary respirator selection system.A system 2400 for selecting a PPE is illustrated, where the system 2400may provide recommendations to a user on which PPE will provide anoptimal fit, or provide a best fit among available PPE. A fit level ofthe PPE upon the user may be determined by a variety of factorsdetermined by the user, employee, and/or manufacturer. For example, anamount of predicted leakage of ambient air through a sealing edge of therespirator may be a determined factor for determining a fit level of therespirator on a facial area of the user. In an exemplary embodiment, ifa respirator were to permit a leakage at a rate beyond a predeterminedthreshold, the respective respirator may be given a low score and/or anon recommendation. In another exemplary embodiment, a force applied bythe respirator upon a facial area of the user may be a determinantfactor on a recommendation of a particular respirator type of size. Ifthe respirator is predicted to apply pressure to the facial area at arate or force exceeding a threshold, the respirator may be given a lowscore and/or a non recommendation because of a possible low comfortlevel provided to the user by the respirator, for example.

The system 2400 may provide fit recommendations or scores based uponcaptured and analyzed images of the user body part (e.g., facial area)and PPE (e.g., respirator). In the depicted example, the system 2400include one or more image capture devices 2405 for capturingrepresentations of a user body part 2410 and/or a type of PPE 2415. Inthe depicted example, the user body part 2410 is a user facial area. ThePPE 2415 may be a respirator, for example. In an exemplary embodiment, aseries of two-dimensional (2D) images may be captured by the imagecapture device 2405 from which a three-dimensional (3D) image may beassembled. In other exemplary embodiments, a 2D image may be used todetermine a PPE fit. In other exemplary embodiments, the image capturedevice may capture a 3D representation of the body part 2410 and/or thePPE 2415. In some examples, facial coordinate data representative of ashape of a facial area and respirator coordinate data representative ofa respirator shape may be analyzed to provide a fit recommendation. Inan exemplary embodiment, the system 2400 may load previously capturedand/or generated body parts 2410 and/or PPE 2415.

The system 2400 may be used for selecting a variety of PPE 2415 to beworn on the intended body part 2410. For example, in certain embodimentsthe system 2400 may predictively choose an optimal fitting glove to fita user hand. In other exemplary embodiments, the system may choose anoptimally fitting helmet to fit a head of a user. In an exemplaryembodiment, several respirator point cloud data sets each indicative ofa specific size and shape respirator 2415 may be stored in a database2420. For example, each respirator that an employer offers to employeesmay be analyzed with associated representative point cloud data, wherethe representative point cloud data may be stored in a database 2420. Inan exemplary embodiment, the point cloud data may include x, y, zcoordinates which may be assembled to form a 3D image of the intendedPPE 2415 and/or user body part 2410. In an exemplary embodiment, thedatabase 2420 may be accessible over a wide-area network (e.g.,Internet) to permit a wide selection of PPE 2415 to users without theneed to personally capture data representative of each eligible PPE2415.

A comparator module 2425 compares the PPE 2415 with the body part 2410to determine whether the PPE 2415 will properly fit the respective bodypart 2410. In an exemplary embodiment, the PPE 2415 is overlaid upon thebody part 2410. For example, a point cloud and/or vertices may bealigned between the PPE 2415 and the body part 2410. In an exemplaryembodiment, the comparator module 2425 uses a set of predetermined rulesfrom a rules library 2430 to determine whether the PPE 2415 properlyfits the body part 2410. For example, the rules may require the sealingedge of a respirator not to be in contact with the mouth of the user. Inanother exemplary embodiment, the rules may require the respirator tohave a surface area as large as the respirator-receiving portion of thefacial area of the user. In another exemplary embodiment, the rules mayidentify a captured body part, such as for example a facial area, anddirect the comparator module to only compare respirators from thedatabase with the body part. In another exemplary embodiment, the rulesmay identify a captured body part, such as for example a hand, anddirect the comparator module to only compare gloves from the databasewith the body part and not to compare respirators with the captured bodypart (e.g., hand).

After a fit of the evaluated PPE 2415 and body part 2410 has beendetermined, a simulator module 2435 may display the fit. For example,the simulator module 2435 may display a representation of the respiratorworn by the specific facial area of the user. In some examples, apredicted tightness or looseness of the PPE 2415 relative the body part2410 may be emphasized in the simulator module 2435. For example, apredicted leakage between the sealing edge of the respirator and thefacial area may be emphasized. A report 2440 may be outputted to theuser to assist in providing a recommendation on fit levels of eachcompared PPE 2415. In some examples, a list of evaluated PPE 2415 may beincluded in the report 2440 with each of the evaluated PPE 2415 having ascore or fit level assigned. In some examples, only recommended PPE 2415may be provided in the report 2440. In some examples, only the highestscoring three or five PPE 2415 may be provided in the report 2440.

FIG. 25 depicts a flowchart of an exemplary PPE selection system. In anexemplary PPE selection system 2500, a determination may be made ofwhether one or more PPE models fit a specific body part. In an exemplaryembodiment, the determination may be made by software operated on aprocessor module. For example, the software may determine whichrespirator of a plurality of respirator models from a respirator typedatabase fits a specific facial area of a user and output arecommendation to a user.

More specifically, data representing an exemplary body part may becaptured as in step 2505. In an exemplary embodiment, the data may becaptured by an image capture device. For example, an image capturedevice may scan a body part to build a 3D representation of the bodypart. In another exemplary embodiment, data representing the body partmay be retrieved or computationally loaded. In an exemplary embodiment,the body part data may be retrieved from a body part database havingspecific body part shapes stored at an earlier date. In other exemplaryembodiments, the data representative of a body part may be a genericbody part computationally generated or morphed from one or more models.In an exemplary embodiment, the representative body part may be a facialarea of a user.

Additionally, data representing one or more types of PPE (e.g., helmets,gloves, PPE) may be captured as in step 2510. In an exemplaryembodiment, the data may be captured by an image capture device. Forexample, an image capture device may scan PPE to build a 3Drepresentation of the PPE. In another exemplary embodiment, datarepresenting the PPE may be retrieved or computationally loaded. In anexemplary embodiment, the PPE data may be retrieved from a PPE databasehaving specific PPE shapes stored at an earlier date. In an exemplaryembodiment, the representative PPE may be a respirator.

A particular type of PPE (e.g., helmets, gloves, PPE) to be worn over orupon the loaded body part may be retrieved or computationally loaded asin step 2515. For example, a PPE type including respirator models may beloaded if the intended body part may be a facial area. In anotherexemplary embodiment, a PPE type having gloves may be loaded if theintended body part may be a hand.

A first PPE model from the loaded relevant PPE type may be retrieved asin step 2520 to be compared via a comparator module with the capturedbody part as in step 2525. Each PPE model may be distinguishable becauseof a size, shape, or other criteria which may affect the fit of the PPEon the user body part. The comparison may determine whether the PPEmodel has a shape that will permit an acceptable fit over the shape ofthe body part. For example, the PPE model may be required to meet orexceed one or more thresholds or rules previously determined asindicative of proper or optimal fitting criteria. In some exemplaryembodiments, the PPE model may be required to fit the body part in bothstatic and dynamic states of the body part.

If the PPE model is determined to fit the body part as illustrated instep 2530, the PPE model may be simulated on the body part as in step2535. In an exemplary embodiment, the PPE model and body part may bedisplayed to the user in a 3D representation. In some exemplaryembodiments, the user may rotate and pan a 3D representation of thesimulated body part and PPE model. In some exemplary embodiments, thesimulated representation may provide visual feedback to the userdetailing areas of the PPE model that are not predicted or determined tofit the respective body part. For example, one or more colors may beoverlaid upon the representation to indicate areas upon the body partthat are predicted to be uncomfortable as a result of wearing the PPEmodel. In other examples, a blinking or flashing area may indicate anarea of the PPE model that does not conform to a minimum thresholddetermined to be required to provide a proper and/or comfortable fit.For example, a portion of a sealing edge of a respirator may blink if aleak is predicted to be present in the respective portion.

After the first PPE model is determined to fit and simulated to theuser, the software may determine if there are any other PPE models inthe chosen PPE type group that are to be evaluated against therespective body part as illustrated by step 2540. If so, the softwarecycles to a second PPE model as illustrated by step 2545 and repeats theprocess of steps 2525-2540. If there are no more PPE models from the PPEtype group, a report and proposal may be generated for output to theuser as illustrated in step 2550. In some exemplary embodiments, thereport and proposal may include the top three PPE models that have thebest fit with respect to the specific body part. In some exemplaryembodiments, the top PPE models or all of the PPE models evaluated maybe provided with a fit score to the user. In some exemplary embodiments,a different PPE type group may require comparison with the body part, inwhich case some or all of the process may be repeated.

FIG. 26 depicts an overview of an exemplary PPE selection system. A PPEselection system 2600 may be used to select an optimal fit PPE for auser body part during dynamic conditions of the user body part. The PPEselection system 2600 includes a device type selection module 2605 forreceiving a command from a user 2610. In an exemplary embodiment, thedevice type selection module 2605 sends commands to a PPE database 2615.The PPE database 2615 may include a variety of types of PPE 2620, suchas for example gloves, respirators, and helmets. In an exemplaryembodiment, the device type selection module 2610 may relay a command2610 indicative of a particular type of PPE 2620, such as for example afacial respirator. In an exemplary embodiment, the command 2610 may beindicative of a particular user body part 2625 to be matched with thePPE 2620 from the PPE database 2615.

In some exemplary embodiments, the device type selection module 2605 maydirect an image capture device (not shown) to capture a 2D or 3D imageof the selected body part 2625. In some embodiments, the PPE 2620 may bemodeled in a corresponding 3D shape. In some exemplary embodiments, oneor more device range rules may define a capture range of the body part2625 for the corresponding PPE 2620. For example, with half-maskrespirators, the device range rules may define a capture range as theuser face. In an exemplary embodiment of multiple PPE candidates, acapture range computing step may calculate a maximum facial area rangethat may accommodate the PPE and then correlate the range with each PPEto determine whether the respective PPE fits within the facial arearange.

Once the user body part is captured or retrieved, such as for examplefrom a database, the user body part may be modeled using a staticmodeling module 2630. The static modeling module generates a 3D model ofthe user body part to be used by a dynamic modeling module 2635. Thedynamic modeling module 2635 communicates with an input module 2640 forgenerating dynamic models of the user body part 2625. In one exemplaryembodiment, the input module 2640 communicates actual body movement 2645to the dynamic modeling module 2635 for generating a dynamic model usingactual movement from the user. In another exemplary embodiment, theinput module 2640 communicates simulated movement 2650 to the dynamicmodeling module 2635 to generate a dynamic model based upon a simulatedbody part movement.

By using a dynamic model, a more realistic fit may be realized betweenthe user body part and the PPE, since a user generally undergoes somemovement while wearing the PPE. The dynamic model may then be comparedwith the PPE models 2620 from the PPE database 2615 by a comparatormodule 2655. The comparator module 2655 may determine a fit level of thePPE model 2620 with the dynamic model from the dynamic modeling module2635 based on a variety of predetermined criteria or rules. For example,the comparator module 2655 may evaluate a size of the PPE model 2620with the dynamic model in an extreme position (e.g., open mouth) todetermine whether the PPE (e.g., respirator) will fit the user body part(e.g., facial area) in the extreme position. The calculated results ofthe comparator module 2655 may be summarized for output andvisualization.

In an exemplary embodiment, the comparator module 2655 may fit the PPEmodel candidate 2620 to the dynamic model set according to mappingrules. The comparator module 2655 may then calculate the differencebetween the PPE model candidate 2620 and dynamic model outputted fromthe dynamic modeling module 2635. According to a set of predeterminedevaluation rules, a fit score of each PPE model 2620 may be providedrelative the dynamic model. Lastly, the comparator module 2655 mayoutput an optimal fit PPE 2620 based on simulated comfort and fit. In anexemplary embodiment, a respective fit of the PPE 2620 may be visualizedby color coding for user.

In an exemplary embodiment, the result from the comparator module 2655may be outputted to a simulator module 2660 for display to a userthrough an output module. In an exemplary embodiment, the simulatormodule may graphically overlay the 3D PPE model 2620 upon a 3Drepresentation of the user body part 2625 to illustrate to the user thePPE model 2620 being virtually worn on the user body part 2625. In someexemplary embodiments, a fit level, score, or color may accompany thegraphical illustration for ease in interpreting the results.

In an exemplary embodiment, the output module 2665 may comprise adisplay module. In some exemplary embodiments, the output module 2665may comprise a printed report. In some exemplary embodiments, the reportmay provide 3D visual representations of the PPE virtually worn by theuser. In some exemplary embodiments, the report may provide a detailedlist of a fit level or score of each evaluated PPE with respect to aregion of interest of the user. In some exemplary embodiments, thereport may provide a color-coded graphical representation of a PPEvirtual fit on the user. In some exemplary embodiments the color-codedgraphical representation may illustrate, through color-coding, differentlevels of pressure as applied to the user by the PPE when virtuallyworn.

FIG. 27 depicts a flowchart of an exemplary process of a static modelingmodule. A static modeling module 2700 may be used to generate anobjective static 3D model of a body part. The static model may be usedin dynamic modeling processes. In some exemplary embodiments, the PPEmay be compared directly to the static model if dynamic comparison isnot necessary. In an exemplary embodiment, a static model of a facialarea may be generated with the static modeling module 2700. In someexemplary embodiments, the generated static model may be in 2D form.

When generating the static model, the module 2700 first captures aregion of interest (ROI) point cloud of a user as in step 2705. The ROImay be the portion of the body that corresponds to the evaluated PPE.For example, when evaluating respirator fit, the ROI may be a facialarea of the user. In an exemplary embodiment, the point cloud mayinclude x, y, z coordinates assembled to form a 3D image of therespective body part.

A generic model may also generated as illustrated in step 2710 togenerically match the body portion captured by the point cloud as instep 2705. For example, if a facial area is the ROI, the generic modelmay be representative of a generic user face. In exemplary embodiments,the generic model may be retrieved from a database of generic models. Insome exemplary embodiments, a preliminary screening process may becompleted to find a generic model being close in shape to the capturedROI. Predetermined semantic information is attributed to the genericmodel as in step 2715. The semantic information may be distinguishablebody feature points of the corresponding body part. For example, afacial area may include semantic information associated with the eyes,ears, mouth corners, and a nose tip. The semantic information may beattributed to the vertices of the generic model, for example. In anexemplary embodiment, a set of rules which define the semanticinformation may include MPEG4 format definition rules.

The point cloud of the ROI is then overlaid on the generic face model bya rigid method as in step 2720. In an exemplary embodiment, the rigidmethod may include a rigid registration or alignment of vertices of theROI and vertices of the generic model. In an exemplary embodiment, thealigned vertices may correspond to proximally similar or equivalentlocations on the modeled body part. For example, the nose portion of thepoint cloud of the ROI may be aligned with nose portion of the genericmodel.

The module 2700 then determines whether the vertices of the point cloudalign or match to an acceptable level or threshold as illustrated instep 2725. For example, if the vertices of the point cloud do notexactly align as determined by a predetermined threshold, the verticesof the point cloud and the generic model are deemed not to align to anacceptable level. If the vertices do not align to an acceptable level,the vertices of the generic model may be deformed to fit the overlaidpoint cloud by a non-rigid registration method. In an exemplaryembodiment, a non-rigid registration method may include blending thenon-aligning vertices of the generic model with neighboring vertices. Inanother exemplary embodiment, certain vertices of the generic model maybe moved a predetermined allowable distance to reach an alignment withthe point cloud of the ROI.

Once alignment is reached with the vertices of the point cloud andvertices of the generic model, the semantic information of each vertexon the generic face model may be attributed to the point cloud. Forexample, the vertices of the point cloud may receive the semanticinformation and be stored within the properties of the point cloud suchthat each of the points in the point cloud may include identificationproperties corresponding to a location of the point in the point cloud.For example a point of the point cloud located at a positioncorresponding to a nose tip may include semantic information identifyingthe point as “nose tip”. The static model having the point cloud withsemantic information may then be outputted as in step 2740. In anexemplary embodiment, the static model may be outputted to the dynamicmodeling module. In another exemplary embodiment, the static model maybe outputted to a comparator module. In yet another exemplaryembodiment, the static model may be outputted to a simulator module. Inan exemplary embodiment, the static model may be a 3D representation.

FIG. 28A depicts a flowchart of an exemplary dynamic modeling moduleusing actual body movement. A dynamic modeling module 2800 uses actualbody movement to generate a dynamic model as described with reference toFIG. 26. In an exemplary embodiment, the dynamic model may be generatedin 3D form. In the depicted example, a user performs actual bodymovement as in step 2805 and an image capture device captures the bodymovement as in step 2810. In some exemplary embodiments, the bodymovement performed may correlate with body movement commonly performedwhile wearing associated PPE. For example, the user may speak a varietyof phrases when fitting a user with a respirator since it may be commonfor a user to speak or move their mouth while wearing the respirator.

Once a series of movements are captured, such as for example by video ora plurality of images, a key frame may be extracted from the capturedsequence as in step 2815. The key frame may be an image reflecting aparticular user movement, for example. In an exemplary embodiment, thekey frame may simply be a generic reference or starting image. In anexemplary embodiment, the user may then manually mark feature points onthe selected, first key frame as in step 2820. The feature points maycorrespond with distinguishable features on the body part captured. Forexample, a nose or mouth may be feature points for a captured facialarea. In some exemplary embodiments, the user manually selects thefeature points by visually selecting the feature points on a computerdisplay. In some exemplary embodiments, the user manually selects thefeature points by selecting body coordinates predetermined to beassociated with the respective feature point. In some exemplaryembodiments, the selection of the feature points may be automated via animage recognition software or device. In some exemplary embodiments, thefeature points may be appointed identifying information, such as forexample semantic information.

Once the feature points of the first key frame are identified andselected, a tracking method may be performed to identify and markfeature points on all key frames based on the selected feature points ofthe first key frame as in step 2825. In an exemplary embodiment, thetracking method may track the feature points via proximity of similarvertices in neighboring key frames. In some exemplary embodiments, thetracking method may be automatically performed by the dynamic module2800.

One of the key frames having feature points may then be selected and thefeature points corresponded to a static 3D model as in step 2830. In anexemplary embodiment, the static 3D model may be generated according tothe detailed process exemplified in FIG. 27. The feature points may belinked to similarly located points from the point cloud of the staticmodel such that properties of the points from the point cloud of thestatic model may be transferred to the feature points of the key frame,for example.

In an exemplary embodiment, a morphed model may then be generated bymorphing the static model to a facial position of the key frame. Forexample, the point cloud of the static model may be altered to aproximal location of the feature points. If the key frame illustrates auser having an open mouth, the static model and associated point cloudmay be altered to reflect an open mouth morphed static model. In anexemplary embodiment, the morphable model may be generated by performingrigid and/or non-rigid registration methods with vertices or pointsbetween the key frame and the static model.

In step 2840, the module 2800 determines whether there are additionalkey frames to analyze. If there are more key frames to analyze, then themodule cycles to the next key frame as in step 2845 and returns to step2830. If there are no more key frames to analyze, then a dynamic modelmay be outputted as in step 2850. In an exemplary embodiment, thedynamic model may be outputted to a comparator module for comparing thePPE model with the dynamic model to determine whether the PPE model fitsthe dynamic model. In an exemplary embodiment, the dynamic model may beoutputted as a 3D model set for all captured key frames.

FIG. 28B depicts an exemplary graphical view of the dynamic modelingmodule of FIG. 28A. As illustrated, a key frame 2855 having featurepoints manually marked on the key frame as described with reference tostep 2820 of FIG. 28A. The static model 2860 may be generated by thestatic modeling module as described with reference to FIG. 27. Asexemplified the point cloud of the static model may be located alongsimilar facial features as the feature points of the key frame. Amorphable model 2865 may be generated by combining the key frame and thestatic model as described with reference to step 2835 of FIG. 28A.

FIG. 29A depicts a flowchart of an exemplary dynamic modeling moduleusing simulated body movement. A dynamic modeling module 2900 usessimulated body movement to generate a dynamic model as described withreference to FIG. 26. In an exemplary embodiment, the dynamic model maybe generated in 3D form. In an exemplary embodiment, an extreme movementset is defined as in step 2905. The extreme movement set may be definedby a user in an exemplary embodiment. In other exemplary embodiments,the extreme movement set may be defined by the PPE manufacturer,regulatory agency, and/or employer. In an exemplary embodiment of arespirator, extreme movements may include raising the head, bowing thehead, speaking, and/or opening the mouth. In an exemplary embodiment,the extreme movement set may be defined according to actions a user maytypically undergo while wearing the respective PPE.

Once the extreme movement set is defined, a first extreme movement maybe selected from the set as in step 2910. Feature points affected by theselected extreme movement are marked or identified on a static model.For example, if the extreme movement selected mimics an open mouth,feature points surrounding a mouth of the static model may be marked oridentified. In an exemplary embodiment, the feature points are linked tocorresponding proximal vertices. The static model may be generated by aprocess as exemplified with reference to FIG. 27, for example.

An influence zone of each feature point may also be defined on thestatic model as illustrated by step 2920. In an exemplary embodiment,the influence zone may be a proximal area of each feature point that maybe affected by movement of the respective vertex. In an exemplaryembodiment, the feature points and/or feature point influence area maycorrespond to predetermined data points of an MPEG 4 standard. In anexemplary embodiment, the feature points may include semanticinformation.

In some exemplary embodiments, the user manually selects the featurepoints by selecting body coordinates predetermined to be associated withthe respective feature point. In some exemplary embodiments, theselection of the feature points may be automated via an imagerecognition software or device. In some exemplary embodiments, thefeature points may be appointed identifying information, such as forexample semantic information.

The maximum feature point position is also defined as in step 2925. Themaximum feature point may correspond to a maximum distance and x, y, zcoordinate location of the feature point away from a normal or currentlocation of the feature point on the static model. The vertices andlinked feature points are then displaced to the maximum position asdefined by the extreme movement as in step 2930 and a morphed model isformed as in step 2935. Under a prior defined deformation functionaffect, neighbor related points on static model are displaced to a newposition. In an exemplary embodiment, the displacement position ofneighbor points can be calculated by:

D _(vertex) =D _(FP) *H(vertex,FP)

where D_(vertex) may be the displacement position of neighbors, D_(FP)is displacement of feature points, H is the deformation function. Theinfluence zone of each feature point may also be blended or alteredaccording to linked feature point movement. In an exemplary embodiment,if a vertex is affected by more than one feature point, neighboringvertices may be blended by a weighted sum.

A deformation function may be defined as:

${H(f)} = \left\{ \begin{matrix}T & {{f} \leq \frac{1 - \beta}{2T}} \\{{\frac{T}{2}\left\lbrack {1 + {\cos \left( {\frac{\pi \; T}{\beta}\left\lbrack {{f} - \frac{1 - \beta}{2\; T}} \right\rbrack} \right)}} \right\rbrack},} & {\frac{1 - \beta}{2T} < {f} \leq \frac{1 + \beta}{2T}} \\0 & {{otherwise}.}\end{matrix} \right.$

where T is the radius of the area that applies the deformation, and βwith the scope of (0, 1) is a parameter to adjust the deformationdegree; if β is close to 1, the deformation will be smooth, and if β isclose to 0, the deformation will be sharp.

In step 2940, the module 2900 determines whether there are additionalkey frames to analyze. If there are more extreme movements to analyze,the module cycles to the next extreme movement as in step 2945 andreturns to step 2910. If there are no more extreme movements to analyze,a dynamic model may be outputted as in step 2950. In an exemplaryembodiment, the dynamic model may be outputted to a comparator modulefor comparing the PPE model with the dynamic model to determine whetherthe PPE model fits the dynamic model. In an exemplary embodiment, thedynamic model may be outputted as a 3D model set for all captured keyframes.

FIG. 29B depicts a graphical view of the exemplary dynamic modelingmodule of FIG. 29A. The exemplary process includes a first definedextreme movement 2955, such as for example an open mouth. A static model2960 may be imported, such as for example the static model generated bythe static model module with reference to FIG. 27. The feature points2965 and influence zones 2970 corresponding to the extreme movement 2955are marked on the static model 2960. In an exemplary embodiment, thefeature points may be located by correspondence with an MPEG 4 standard.A deformation function 2975 may be executed to morph the static model bydisplacing the feature points and influence zone according to themaximum position as defined by the extreme movement. Then, a morpheddynamic model 2980 is outputted. The dynamic model may be generated in3D form. In an exemplary embodiment, the dynamic model has a bodyposition correlating to the defined extreme movement.

FIG. 30 depicts an overview of another exemplary PPE selection system. APPE selection system 3000 may be used to select an optimal fit PPE for auser body part based on an internal space measured between the PPE andthe body part. In an exemplary embodiment, the internal space may bemeasured during a deformed state of the PPE. The PPE selection system3000 includes a device type selection module 3005 for receiving acommand from a user 3010. In the depicted example, the device typeselection module 3005 sends commands to a PPE database 3015. The PPEdatabase 3015 may include a variety of types of PPE 3020, such as forexample gloves, respirators, and helmets. In an exemplary embodiment,the device type selection module 3010 may relay a command 3010indicative of a particular type of PPE 3020, such as for example afacial respirator. In an exemplary embodiment, the command 3010 may beindicative of a particular user body part 3025 to be matched with thePPE 3020 from the PPE database 3015.

In some exemplary embodiments, the device type selection module 3005 maydirect an image capture device (not shown) to capture a 2D or 3D imageof the selected body part 3025. In some embodiments, the PPE 3020 may bemodeled in a corresponding 3D shape. In some exemplary embodiments, oneor more device range rules may define a capture range of the body part3025 for the corresponding PPE 3020. For example, with half-maskrespirators, the device range rules may define a capture range as theuser face. In an exemplary embodiment of multiple PPE candidates 3020, acapture range computing step may calculate a maximum facial area rangethat may accommodate the PPE 3020 and then correlate the range with eachPPE 3020 to determine whether the respective PPE 3020 fits within thefacial area range.

Once the user body part 3025 is captured or retrieved, such as forexample from a database, the user body part 3025 may be modeled using acontact line module 3030. The contact line module 3030 determines acontact line of the edge of the PPE 3020 on the body part 3025 of theuser. For example, a respirator sealing edge may be defined as thecontact line since the sealing edge may be the primary portion of therespirator that makes contact with the user facial area. The contactline may be determined by capturing an image of the user wearing the PPE3020 and not wearing the PPE 3020, and then using a subtractive functionto find the contact line. In an exemplary embodiment, the contact linemay be found by capturing a 2D or 3D image of the user wearing and notwearing the PPE 3020. In another exemplary embodiment, the contact linemay be determined using previously captured models of users and/or PPE3020. For example, the previously captured models may be aligned using arigid or non-rigid registration method to calculate a contact line. Oncethe contact line is found or calculated, the portion of the body part3025 confined by the contact line may be determined. For example, aportion of a face confined and within the contact line of a respiratormay include a portion of a nose and a mouth.

A deformation module 3035 may then be used to deform the PPE 3020. ThePPE 3020 may be deformed according to a set of predetermined rules. Forexample, if a respirator is known to partially collapse inwards acertain percentage during wear, the PPE 3020 model may be deformed anamount or distance equivalent to a calculated standard collapse of anin-use respirator. In another exemplary embodiment, the degree ofdeformation may be determined by a maximum flex permissible by theconstruction of the PPE 3020. In an exemplary embodiment, a deformationof an inside surface or part of the PPE 3020 may be determined orcalculated from a deformation of an outside surface or part of the PPE3020. In another exemplary embodiment, a deformation of an outside partof the PPE 3020 may be computed by comparing the outside part of the PPE3020 to a deformation of the inside part of the PPE 3020.

A comparator module 3055 may determine a fit level of the deformed PPE3020 model 3020 with respect to the portion of the body part 3025internal or confined by the contact line. In comparison, an internalmeasurement may be made between the internal surface of the PPE 3020 andthe portion of the body part 3025 confined or internal to the contactline. For example, a distance between an inside surface of a respiratorand a portion of a user face perpendicular to the inside surface may becalculated while the respirator is in the deformed state. In anexemplary embodiment, the internal measurement may be a distance betweenthe PPE 3020 and the body part 3025. In another exemplary embodiment,the internal measurement may be an internal volume confined between theinside of the PPE 3020 and the corresponding body part 3025. In someexemplary embodiments, the internal measurement may be compared againsta predetermined threshold to determine whether the PPE 3020 meetspredetermined fit criteria. For example, if the predetermined thresholdis not large enough, the PPE 3020 may be disqualified from an acceptablefit category of PPE 3020. The calculated results of the comparatormodule 3040 may be summarized for output and visualization.

In an exemplary embodiment, the internal measurement may use an implicitfunction to calculate a distance between the inside part of the PPE 3020and the corresponding body part 3025. A Gaussian smooth function maythen be applied to the distance calculation, for example. In someexemplary embodiments, a color-coded result of the internal measurementmay be outputted to a user.

In an exemplary embodiment, the comparator module 3040 may fit the PPE3020 model candidate 3020 to the user body part 3025 set according tomapping rules. According to a set of predetermined evaluation rules, afit score of each PPE 3020 model 3020 may be provided relative the userbody part 3025. Lastly, the comparator module 3040 may output an optimalfit PPE 3020 based on simulated comfort and fit. In an exemplaryembodiment, a respective fit of the PPE 3020 may be visualized by colorcoding for user.

In an exemplary embodiment, the result from the comparator module 3040may be outputted to a simulator module 3045 for display to a userthrough an output module. In an exemplary embodiment, the simulatormodule may graphically overlay the 3D PPE 3020 model 3020 upon a 3Drepresentation of the user body part 3025 to illustrate to the user thePPE 3020 model 3020 being virtually worn on the user body part 3025. Insome exemplary embodiments, a fit level, score, or color may accompanythe graphical illustration for ease in interpreting the results.

In an exemplary embodiment, the output module 3050 may comprise adisplay module. In some exemplary embodiments, the output module 3050may comprise a printed report. In some exemplary embodiments, the reportmay provide 3D visual representations of the PPE 3020 device virtuallyworn by the user. In some exemplary embodiments, the report may providea detailed list of a fit level or score of each evaluated PPE 3020device with respect to a region of interest of the user. In someexemplary embodiments, the report may provide a color-coded graphicalrepresentation of a PPE 3020 device virtual fit on the user. In someexemplary embodiments the color-coded graphical representation mayillustrate, through color-coding, different levels of pressure asapplied to the user by the PPE 3020 device when virtually worn.

FIG. 31 depicts a flowchart of another exemplary PPE selection system.In the exemplary system 3100, an optimal fit PPE for a user body partbased on an internal space measured between the PPE and the body part.The system provides a method of measuring an internal or hidden spacebetween a PPE and an associated body part to generate an objective fitof the PPE on the body part. For example, a point cloud of an outsidepart of a PPE may be captured during a deformed and non deformed stateto determine a position or shape of an inside part of a PPE. In thedepicted example, a point cloud may be captured of a region of interest(ROI) of a body part of the user as in step 3105. For example, a pointcloud may be captured of a facial area of the user. A point cloud may becaptured of a PPE as in step 3110, such as for example a respirator. Inan exemplary embodiment, a point cloud may be captured both of theoutside part (e.g., outside surface) and the inside part (e.g., insidesurface) of the PPE. A point cloud may also be captured of the ROI withthe PPE being worn as in step 3115.

In an exemplary embodiment, the point cloud data may include x, y, zcoordinates which may be assembled to form a 3D image of the intendedPPE and/or user body part. In an exemplary embodiment, a point cloud mayinclude semantic information or other identifying feature points of theuser body part and/or PPE. In some exemplary embodiments, an imagecapture device may directly capture a 2D or 3D image of the selectedbody part ROI and/or PPE. In some exemplary embodiments, previouslycaptured 2D or 3D images of body part ROI and/or PPE may be used. In anexemplary embodiment, when there is not a 3D PPE model, captured pointcloud of people with and without device may be retrieved independentlyand compared to get placement information for the outside part of PPE.An estimate of placement and fit of the inside part of PPE may be made,for example.

A contact line may also be defined as in step 3120. The contact line maybe the point or edge that the PPE makes contact with the user ROI, suchas for example a sealing edge of a respirator on a face of a user. Oncethe contact line is determined a portion of the ROI that is confined orwithin the contact line may be determined as will be described.

A deformation of the PPE may also be calculated, measured, or determinedas in step 3125. For example, a deformation of an inside or outside partof the PPE may be calculated or measured based on a deformation of arespective outside or inside part of the PPE. In an exemplaryembodiment, a degree of deformation may be predetermined by amanufacturer. In another exemplary embodiment, a degree of deformationmay be determined by an employer based on common workplace practices. Inan exemplary embodiment, the inside or outside part of the PPE may beused to generate the deformed PPE structure, thus only one of the insideor the outside part of the PPE may be needed.

The internal space between the PPE and the portion of the ROI confinedby the contact line may then be measured as in step 3130. In anexemplary embodiment, the internal space may be determined based on aperpendicular distance between the PPE and the ROI. In another exemplaryembodiment, the internal space may be determined by a contained volumebetween the PPE and the ROI. In an exemplary embodiment, the internalspace may be measured while the PPE is in a deformed state.

A comparator module may determine whether a threshold has been met bythe measured internal space as in step 3135. If a predeterminedthreshold has been met, then a positive recommendation may be outputtedto a user as in step 3140. In an exemplary embodiment, a 3D visualrepresentation of the PPE on the ROI may be displayed to the user. Inanother exemplary embodiment, the internal measurement may be displayedon the 3D visual representation. If a predetermined threshold has notbeen met, then a negative recommendation may be outputted to a user asin step 3145. For example, if the distance between an internal surfaceof a respirator and the beneath facial area does not meet apredetermined length, then the respirator may fail a fit test.

FIG. 32A depicts a flowchart of another exemplary PPE selection system.In the exemplary system 3200, an optimal fit PPE for a user body partbased on an internal space measured between the PPE and the body part.In the depicted example, the system 3200 may compute a fit of the PPE onthe user body part based on previously captured point clouds of the PPEand/or body part. In an exemplary embodiment, point cloud data may becaptured of the body part ROI (e.g., face) and the PPE (e.g.,respirator) as in step 3205. In some exemplary embodiments, point clouddata may be captured of the ROI with the PPE being worn.

If the PPE is not presently available, previously captured and storedpoint cloud data may be used to determine placement of the PPE on theROI. For example, point cloud data of the PPE may be overlaid upon pointcloud data of the ROI as in step 3210. In an exemplary embodiment, thepoint cloud data may be aligned using a rigid-registration method. In anexemplary embodiment, the rigid-registration method aligns featurepoints of the PPE and ROI. In another exemplary embodiment, therigid-registration method aligns semantic information of the PPE andROI. In another exemplary embodiment, the rigid-registration methodaligns corresponding vertices of the PPE and ROI.

In an exemplary embodiment, once the PPE is placed on the ROI, a contactline of the PPE on the ROI may be obtained as in step 3215. The contactline may be the point or edge that the PPE makes contact with the userROI, such as for example a sealing edge of a respirator on a face of auser.

In an exemplary embodiment, the contact line may be visibly orcomputationally defined and such that a center part of the contact linemay be determined. For example, the center part of the contact line maybe the center of a medial axis of the contact line. In an exemplaryembodiment, the medial axis may be vertically oriented and separate leftand right sides of the space confined by the contact line. A center partof an inside part of the PPE may also be computationally determined andthe center part of the PPE and the center part of the ROI are aligned.In an exemplary embodiment, rigid-registration methods may be used toobtain placement of the PPE on the ROI by aligning the center parts ofthe PPE and the ROI. Once the center parts of the ROI and PPE arealigned, corresponding points of the ROI and PPE may be determined andconfirmed such as for making internal measurements.

A deformation of the PPE may be calculated, measured, or determined asin step 3230. For example, a deformation of an inside or outside part ofthe PPE may be calculated or measured based on a deformation of arespective outside or inside part of the PPE. In an exemplaryembodiment, a degree of deformation may be predetermined by amanufacturer. In another exemplary embodiment, a degree of deformationmay be determined by an employer based on common workplace practices. Inan exemplary embodiment, the inside or outside part of the PPE may beused to generate the deformed PPE structure, thus only one of the insideor the outside part of the PPE may be needed.

The internal space between the PPE and the portion of the ROI confinedby the contact line may then be measured as in step 3235. In anexemplary embodiment, the internal space may be determined based on aperpendicular distance between the PPE and the ROI. In another exemplaryembodiment, the internal space may be determined by a contained volumebetween the PPE and the ROI. In an exemplary embodiment, the internalspace may be measured while the PPE is in a deformed state.

A comparator module may determine whether a threshold has been met bythe measured internal space as in step 3240. If a predeterminedthreshold has been met, then a positive recommendation may be outputtedto a user as in step 3245. In an exemplary embodiment, a 3D visualrepresentation of the PPE on the ROI may be displayed to the user. Inanother exemplary embodiment, the internal measurement may be displayedon the 3D visual representation. If a predetermined threshold has notbeen met, then a negative recommendation may be outputted to a user asin step 3250. For example, if the distance between an internal surfaceof a respirator and the beneath facial area does not meet apredetermined length, then the respirator may fail a fit test.

FIG. 32B depicts an exemplary center part on a ROI as defined withreference to FIG. 32A. A region of interest ROI 3255 may be a body partthat is to be protected, such as by a corresponding PPE. In an exemplaryembodiment, the ROI 3255 may be a facial area of a user. The ROI 3255may be illustrated in a 3D form to a user. In an exemplary embodiment,the ROI 3255 includes point cloud data used in the construction of the3D form and the fitting of the PPE.

In an exemplary embodiment, a contact line 3260 may be defined on theROI 3255, as previously defined with reference to step 3215 of FIG. 32A.The contact line 3260 may be peripheral edge of the PPE that makescontact with the ROI 3255, such as for example a sealing edge of arespirator. In an exemplary embodiment, the contact line 3260 may becomputationally determined by comparing a user ROI while wearing andwhile not wearing a PPE. In another exemplary embodiment, the contactline 3260 may be manually drawn on the ROI by tracing a peripheral edgeof the PPE worn on the ROI.

A center part 3265 of the contact line 3260 may also be defined, aspreviously defined with reference to step 3220 of FIG. 32A. In anexemplary embodiment, the center part 3265 includes a medial axis 3270and axis center 3275. The medial axis 3270 may separate two-halves ofthe area of the ROI 3255 defined by the contact line 3260. For example,the medial axis 3270 may separate left and right halves of the area ofthe ROI 3255 defined by the contact line 3260. In an exemplaryembodiment, the axis center 3275 may be the lengthwise center of themedial axis 3270.

In an exemplary embodiment, a PPE center part including a PPE medialaxis and PPE axis center are also defined on the PPE with reference to acontact edge (e.g., sealing edge of a respirator). The PPE medial axisand PPE axis center of the PPE are then aligned with the medial axis3270 and axis center 3275 of the ROI to determine a placement of the PPEon the ROI, as previously defined with reference to step 3225 of FIG.32A.

FIG. 33 depicts a flowchart of an exemplary deformation process. Adeformation module 3300 may determine a deformation of an inside part ofPPE by correspondence with a deformation of an outside part of the PPEto obtain an internal measurement of the PPE and body part fordetermining whether the PPE fits the body part. The module 3300 firstcaptures point cloud data of the body part and PPE as in step 3305. Insome exemplary embodiments, the point cloud data may be captured earlierin the process and relayed to the deformation module 3300. The pointcloud sets are overlaid upon each other as in step 3310 to fit the PPEto the body part. In some exemplary embodiments, the fitting process maybe performed by other modules and the result relayed to the deformationmodule 3300.

The module 3300 may then obtain deformation parameters of the outsidepart of the PPE as in step 3315. In an exemplary embodiment, the outsidepart of the PPE may be an outside surface of the PPE with respect to thePPE being worn by a user. In an exemplary embodiment, the deformationparameters may be predetermined according to specific constructionproperties of the PPE. In another exemplary embodiment, the deformationparameters may be determined by functions, such as for example thedeformation function described with reference to FIG. 29A.

The PPE outside part may then be corresponded to the PPE inside part asin step 3320. For example, corresponding inside and outside part pointsmay be correlated based upon a nearest distance between inside pointsand outside mesh nodes. In another exemplary embodiment, inside pointsor vertices determined to be physically affected by specific outsidepoints or vertices are linked. For example, moving a point A on anoutside part may correspondingly move a point B on an inside part of thePPE, and thus point A may be linked to some degree to point B.

Once all necessary inside and outside part points of the PPE have beenlinked, the outside point deformation parameters previously defined instep 3315 are attributed to the respective inside points as in step3325. The PPE inside part may then be computationally deformed as instep 3330. In an exemplary embodiment, the PPE inside part may bedeformed according to the attributed deformation parameters linked tothe respective inside part in step 3325.

The internal space between the inside part of the PPE and the portion ofthe ROI confined by the contact line may then be measured as in step3335. In an exemplary embodiment, the internal space may be measuredwhile the inside part of the PPE is in the deformed state. In anexemplary embodiment, the internal space may be determined based on aperpendicular distance between the PPE and the ROI. In another exemplaryembodiment, the internal space may be determined by a contained volumebetween the PPE and the ROI.

A comparator module may determine whether a threshold has been met bythe measured internal space as in step 3340. If a predeterminedthreshold has been met a positive recommendation may be outputted to auser as in step 3345. In an exemplary embodiment, a 3D visualrepresentation of the PPE on the ROI may be displayed to the user. Inanother exemplary embodiment, the internal measurement may be displayedon the 3D visual representation. If a predetermined threshold has notbeen met, then a negative recommendation may be outputted to a user asin step 3350. For example, if the distance between an internal surfaceof a respirator and the beneath facial area does not meet apredetermined length, then the respirator may fail a fit test.

FIG. 34 depicts a graphical representation of an exemplary color-codeddisplay of a PPE fit. A display 3400 may be outputted to a user by anoutput module for providing a visual recommendation of a PPE fit. In anexemplary embodiment, the display 3400 may be outputted on a computerscreen. In another exemplary embodiment, the display 3400 may beoutputted in a printable format.

The display 3400 includes a representation of the evaluated user bodypart 3405, for example a facial area. In an exemplary embodiment, thebody part 3405 may be portrayed in 3D form. The body part 3405 may becolored according to pressure distribution as applied on the body part3405 by the PPE. In an exemplary embodiment, the PPE may be shown withthe body part 3405. In the depicted example, the display 3400 includes areference chart 3410 of the colors illustrated on the body part 3405 andvalues 3415 associated with each of the colors on the color chart 3410.The values 3415 may represent ranges of pressure distribution, forexample.

In an exemplary embodiment, a user may visually determine whether a PPEwould provide an acceptable fit by visualizing whether any areas uponthe body part 3405 are a certain color. For example, if an area of thebody part 3405 were colored red, a high degree of applied pressure maybe applied to the body part 3405 by the respective PPE. For example, arespirator may fit tightly against a face of a user in a certain area.In an exemplary embodiment, if a certain color were displayed on thebody part 3405 which would represent a threshold being exceeded, therespective PPE may be disqualified from further consideration withrespect to the specific user.

In another exemplary embodiment, shapes or symbols, rather than colorsmay be visually displayed on the body part 3405 to symbolize measuredcriteria. For example, a first shape may represent a first pressureapplied to the body part 3405 by the PPE and a second color mayrepresent a second pressure applied to the body part 3405 by the PPE. Inanother exemplary embodiment, a first color, shape, or pattern may beoverlaid upon the body part 3405 to represent a first distance that thePPE is from the body part when virtually worn, and a second color,shape, or pattern may be overlaid upon the body part 3405 to represent asecond distance that the PPE is from the body part when virtually worn.

FIG. 35 depicts a flowchart of an exemplary color-coded resultgenerator. A color-coded result generator 3500 may associated a set offit results with one or more colors to provide a user with a quickmethod of determining whether a respective PPE would fit and/or becomfortable. The fit results may be calculated and assigned to each ofthe points on the point cloud, such that each point in the point cloudof the ROI may have an associated fit result as illustrated in step3505. The result generator 3500 may calculate and assign the fit resultsor the fit results may be imported. In an exemplary embodiment, the fitresults each include an applied pressure upon the body part by the PPE.For example, the fit results may include a pressure applied to a facialarea by the respirator at each defined point or vertices.

The generator 3500 may correlate one or more colors to one or morepredetermined ranges as in steps 3510 and 3515. For example, a firstrange of applied pressure values may be assigned a first color, forexample a blue color. A second range of applied pressure values may beassigned a second color, for example a green color. The generatordetermines whether more colors are needed as in step 3520 and generatesadditional colors with assigned predetermined ranges as in step 3525. Inanother exemplary embodiment, a set of predetermined colors may beinitially assigned that include all possible ranges, such as for examplefrom −∞ to +∞.

The color ranges may then be corresponded to the fit results as in step3530. For example, a green color overlay on the human body part mayrepresent an optimal match and a red color overlay on the human bodypart may represent a non optimal match. In an exemplar embodiment, thecolors may represent how tight or loose PPE may be relative the humanbody part, such as for example red being shown for an area of the bodypart where the PPE product fits too tightly and green may be shown foran area of the body part where the PPE product fits too loosely.

The colors may then be overlaid on a body part ROI representation as instep 3535 and the result may be outputted to the user as in step 3540.In an exemplary embodiment, the body part ROI representation may be in3D form. An exemplary output is shown by display 3400 of FIG. 34.

Although various embodiments have been described with reference to theFigures, other embodiments are possible. For example, in someembodiments, the system and method for automatically selecting arespirator may comprise predictive software that may capture a facialimage and match the facial image to the closest form of a respiratormodel, type, and/or size. In an exemplary embodiment, the software mayuse a dynamic set of images and match the images to the flexibility of arespirator shape to predict an interface between the respirator modeland the facial model. For example, the software may predict whether theinterface between the respirator and the facial area will result inseparation thus permitting leakage or breach in the sealing surface.

In various embodiments, the image capture device may be a 3D digitalscanner, such as for example one or more Kinect devices manufactured byMicrosoft®. In some embodiments, the image capture device may be a stillcamera device. In some exemplary embodiments, the image capture devicemay be a video recorder device. In some exemplary embodiments, the imagecapture device may be a handheld unit. The image capture device may bewirelessly connected to a processing module for receiving a scannedimage from the image capture device and determining whether a scanned ormodeled PPE fits a scanned or modeled body part. In some exemplaryembodiments, the image capture device may be a low-cost item.

In various embodiments, apparatus and methods may involve a digitalimage of a facial area of a user in a variety of facial positions. Forexample, a first facial position may be a grin or smile. A second facialposition may be the user voicing specific letters and/or sounds. In anexemplary embodiment, software may digitize the facial shape of the userin each of the facial positions to create a flexible electronic file,for example. In an exemplary embodiment, software may also store fileshaving contours of respirators in both a static state and in a flexedstate for comparison to facial shape files. In an exemplary embodiment,the software may match up a negative cavity of the respirator model witha positive face form of the facial area model to determine a fit levelof a respirator or best fit respirator. In some exemplary embodiments,software may match the respirator and the facial area in both static anddynamic positions of the facial area and/or respirator to determinewhether a respirator will fit in a variety of facial positions and/orflexed positions of the respirator.

In an exemplary embodiment, an administrator may oversee a matchingprocess of the respirator and a specific facial area. For example, anadministrator at a workplace may oversee the matching process for eachnew employee. In some examples, each employee may undergo a matchingprocess, such as for example via a pay per use web link. In someexemplary embodiments, a kiosk or vending machine may include softwarefunctionality to perform a matching process between one or morerespirators and a specific facial shape. For example, a user may scan auser facial shape at a kiosk, and the kiosk may geometrically comparethe facial shape of the user to a plurality of respirator modelsavailable for dispensing to find a respirator that most closely matchesthe facial shape of the user. Upon finding an optimal or best fitrespirator, the kiosk may dispense the respective respirator or providedirection to the user on where the respirator may be available forpickup and/or purchase, for example.

In accordance with another embodiment, a population data gathering andstorage system may be made available via scanning facial areas of users.In some examples, the facial shapes gathered and stored via the matchingprocess may be used by respirator manufacturers to improve a respiratordesign such that newly manufactured respirators more closely match acommon facial shape of persons commonly wearing the respirators. In someexamples, the facial shapes gathered and stored via the matching processmay be used by employers to provide insight on which respirators tostock in greater or less numbers. In some exemplary embodiments, acaptured point cloud of a PPE and/or a user body part may be re-used inother PPE design.

In accordance with another embodiment, a variety of body parts may bescanned and captured for being matched with respective clothing orgarments. For example, a hand of a user may be scanned and stored as adata set such that a variety of glove models, types, and/or sizes may becompared against the hand of the user to find an optimal or best fitglove. In another exemplary embodiment, a head of a user may be scannedand stored as a data set such that a variety of helmet models, types,and/or sizes may be compared against the head of the user to find anoptimal or best fit helmet.

In accordance with an exemplary embodiment, a system and method forselecting a respirator may include a body modeling module for capturingan image(s) of a body part (e.g., facial area) of a user. In anexemplary embodiment, the image(s) may be used to generate a 3D model ofthe body part.

In some embodiments, the system and method for selecting a respiratormay include one or more product databases of PPE 3D models. For example,each product database may include PPE to be worn on a specific bodypart. In an exemplary embodiment, a respirator database may beassociated with facial areas, a glove database may be associated withhands, and a helmet database may be associated with heads. In someexemplary embodiments, the material properties of each specific PPE mayalso be stored with the specific PPE model.

In some embodiments, the system and method for selecting a respiratormay include a rule library illustrating a method of mapping 3D PPEmodels to a 3D human body part. In an exemplary embodiment, a rulelibrary may include three types of rules, such as for exampleassociation rules, mapping rules, and evaluation rules. For example,association rules may define which related PPE 3D models from theproduct database are associated to a target body part. For example,respiratory products from product database may be associated to facemodels, and footwear products from product databases may be associatedto foot models. In an exemplary embodiment, mapping rules may define howthe product model will be mounted to the body model, such as for exampleby mapping directions, forces, and/or deformations according to amaterial property. In an exemplary embodiment, evaluation rules maydefine how well the PPE fits the body part in accordance with a mappingresult. For example, via dimensional comparison, a body dimension may becompared to a related product dimension range or pressure distributionduring and after the product is mapped to the body part.

In some embodiments, the system and method for selecting a respiratormay include a 3D geometry matching module. In an exemplary embodiment,the matching module may calculate all differences between the 3D PPEmodels and the 3D human body model. The geometry matching module mayselect a PPE part according to association rules, determine thedifference with the mapping rules, summarize the difference according tothe evaluation rules, and then propose a product model and/or size whichmay optimally fit a user. In an exemplary embodiment, a top three or topfive best fitting products may be provided to the user.

In some embodiments, the system and method for selecting a respiratormay include a simulator module. In an exemplary embodiment, a simulatormodule may visualize to a user how well the PPE model fits on the bodypart model. In some exemplary embodiments, the simulator may display thehuman body part and PPE product in 3D representations. In some exemplaryembodiments, color coding may be used to illustrate how well the PPEfits a human body part. For example, a green color overlay on the humanbody part model may represent an optimal match and a red color overlayon the human body part model may represent a non optimal match. In someexamples, the colors may represent how tight or loose the PPE may berelative the human body part, such as for example red being shown on anarea of the body part model where the PPE fits too tightly and greenshown on an area of the body part model where the PPE fits too loosely.

In accordance with an exemplary embodiment, the PPE selection system mayoutput a comfort level based on a predetermined measurement scale, wherethe comfort level may reference a relative comfort of a PPE virtuallyworn by a user. In some embodiments, a comfort level may be determinedby the amount of internal space measured between an inside part of a PPEand a corresponding body part. In some exemplary embodiments, a comfortlevel may be determined by a degree of permissible movement by arespective body part while a PPE is worn. For example, a comfort levelmay be determined for a respirator by determining whether the respiratormaintains a seal with a facial area while the mouth of the user is beingopened. In accordance with an exemplary embodiment, a user feeling maybe determined by an objective comfort evaluation based on quantitativemeasurement. For example, a module may calculate a numeric pressurelevel upon the facial model as applied by the respirator model andcompare the calculated pressure level with a set of predeterminedpressure ranges each associated with a specific comfort level.

A PPE selection system may be used for predicting an optimal fitting PPE(e.g., respirator) for a specific user. The system may includes anoffline phase and a selection phase. The offline phase may be performedduring a fitting process of the PPE to the user in some exemplaryembodiments. In other exemplary embodiments, the offline phase may beperformed at some time prior to the fitting process of the PPE to theuser.

In the offline phase, one or more types of PPE may be selected to beassociated with semantic information as in step. In an exemplaryembodiment, several PPE are analyzed and processed to build a databaseof PPE having semantic properties. In some examples, the PPE types mayinclude a variety of sizes and models of face respirators (e.g., masks).A volunteer face model having semantic properties may then be correlatedwith the specific PPE such that the semantic properties of the facemodel are correlated with intersecting points or vertices of thespecific PPE as depicted in step. To increase the accuracy of thelocations of the semantic properties applied to the specific PPE model,the volunteer face model may be chosen based on how well the specificPPE fits the volunteer face model. For example, if the specific PPE fitsthe volunteer face model well, then the respective volunteer face modelmay be used.

In the selection phase, a specific PPE may be compared to a specificuser model to determine a fit of the PPE with respect to a specific usermodel. A region of interest to receive the PPE may be defined on theuser. The region of interest may be the face of the user. The region ofinterest may be captured and modeled in a 3D format. For example, ascanning device or image capture device may scan the face of the userand form a 3D model of the user's face through one or a series ofimages. A point cloud set may be defined on the 3D model of the user andsemantic properties are applied to one or more of the defined points togenerate a semantic face model as shown in step.

Also in the selection phase, a database may be accessed to retrieve aspecific PPE model to be compared to the specific user model, where thePPE model has semantic properties as shown by respirator model. In anexemplary embodiment, the database may be populated in the offline phasewith a multitude of PPE models each representative of a specific modeland size PPE and each having semantic properties.

The respirator model and the face model may be aligned to determine afit. For example, the respirator model may be superimposed upon the facemodel. The locations of the semantic properties of the respirator modeland the face model may be then compared to determine a fit level of therespirator model to the face model as in step. In an exemplaryembodiment, the distance between points on the respirator model and theuser face model each having the same semantic properties may bedetermined for assessing a fit level of the respirator model to the userface model. Once an acceptable number of respirator models have beenevaluated against the specific user face model, the respirator modelhaving the best fit or highest fit level may be chosen for the specificuser to wear as in step.

A final fit score determination process computes an overall fit score ofPPE being virtually worn by a user, for example a 3D respirator modelrepresentative of a specific respirator being virtually worn by a 3Dfacial model representative of a specific user. In an exemplaryembodiment, the determination process may include a graphical interfaceadapted for control by an administrator. For example, the administratormay individually control a variety of parameters to determine whetherthe 3D respirator model fits the 3D face model.

In some examples, determination of whether the respirator model fits the3D face model may be determined in response to a calculated estimatedlevel of comfort parameter, an estimated level of face-seal parameter,and an amount of dead space parameter. In an exemplary embodiment, theestimated level of comfort parameter may be a degree of comfort felt bythe user calculated by evaluating a contacting portion of the respiratormodel against the face model. In an exemplary embodiment, the estimatedlevel of face-seal parameter may be a calculated gap between a seal ofthe respirator model and the face model. In an exemplary embodiment, theamount of dead-space parameter may be a calculated internal distancebetween the respirator model and the face model.

Based on the parameters, a final fit score may be calculated. In anexemplary embodiment, the final fit score may be representative of howwell the respirator model fits the face model, or the perceived feelingor comfort of the user while wearing the respirator. In an exemplaryembodiment, a final fit score of 100% may be representative of a perfectfit of the respirator model on the face model, and a final fit score of0% may be representative of a worst-case fit of the respirator model onthe face model. In an exemplary embodiment, a final fit score of 75% maybe representative of a very good fit of the respirator model on the facemodel and a final fit score of 25% may be representative of a belowaverage fit of the respirator model on the face model.

In some examples, each parameter may be weighted by a predeterminedweighted function to arrive at a parameter result. For example, theestimated level of comfort may have a weighted function of 0.4. Sincethe estimated level of comfort parameter for the particular respiratormodel may be 35%, for example, the parameter result may become35%×0.4=14%. Each parameter result may be totaled to arrive at the finalfit score. In an exemplary embodiment, the weighted function may bechanged for a particular type of respirator or particular type of PPE.In another exemplary embodiment, the weighted function may bepredetermined by the administrator or the user based on company or userpreferences.

In some examples, a 3D representation of the respirator model being wornby the face model may be illustrated in the graphical interface fordisplay to the user. In an exemplary embodiment, a screenshot orprintout of a PPE fit result may be provided to the user.

Some aspects of embodiments may be implemented as a computer system. Forexample, various implementations may include digital and/or analogcircuitry, computer hardware, firmware, software, or combinationsthereof. Apparatus elements can be implemented in a computer programproduct tangibly embodied in an information carrier, e.g., in amachine-readable storage device, for execution by a programmableprocessor; and methods can be performed by a programmable processorexecuting a program of instructions to perform functions of variousembodiments by operating on input data and generating an output. Someembodiments can be implemented advantageously in one or more computerprograms that are executable on a programmable system including at leastone programmable processor coupled to receive data and instructionsfrom, and to transmit data and instructions to, a data storage system,at least one input device, and/or at least one output device. A computerprogram is a set of instructions that can be used, directly orindirectly, in a computer to perform a certain activity or bring about acertain result. A computer program can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, device driver, or other unit suitablefor use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example and not limitation, both general and specialpurpose microprocessors, which may include a single processor or one ofmultiple processors of any kind of computer. Generally, a processor willreceive instructions and data from a read-only memory or a random accessmemory or both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Storage devices suitable for tangibly embodying computerprogram instructions and data include all forms of non-volatile memory,including, by way of example, semiconductor memory devices, such asEPROM, EEPROM, and flash memory devices; magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; and,CD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, ASICs (application-specificintegrated circuits). In some embodiments, the processor and the membercan be supplemented by, or incorporated in hardware programmabledevices, such as FPGAs and PLDs, for example.

In some implementations, each system may be programmed with the same orsimilar information and/or initialized with substantially identicalinformation stored in volatile and/or non-volatile memory. For example,one data interface may be configured to perform auto configuration, autodownload, and/or auto update functions when coupled to an appropriatehost device, such as a desktop computer or a server.

In some implementations, one or more user-interface features may becustom configured to perform specific functions. An exemplary embodimentmay be implemented in a computer system that includes a graphical userinterface and/or an Internet browser. To provide for interaction with auser, some implementations may be implemented on a computer having adisplay device, such as an LCD (liquid crystal display) monitor fordisplaying information to the user, a keyboard, and a pointing device,such as a mouse or a trackball by which the user can provide input tothe computer.

In various implementations, the system may communicate using suitablecommunication methods, equipment, and techniques. For example, thesystem may communicate with compatible devices (e.g., devices capable oftransferring data to and/or from the system) using point-to-pointcommunication in which a message is transported directly from the sourceto the receiver over a dedicated physical link (e.g., fiber optic link,point-to-point wiring, daisy-chain). The components of the system mayexchange information by any form or medium of analog or digital datacommunication, including packet-based messages on a communicationnetwork. Examples of communication networks include, e.g., a LAN (localarea network), a WAN (wide area network), MAN (metropolitan areanetwork), wireless and/or optical networks, and the computers andnetworks forming the Internet. Other implementations may transportmessages by broadcasting to all or substantially all devices that arecoupled together by a communication network, for example, by usingomni-directional radio frequency (RF) signals. Still otherimplementations may transport messages characterized by highdirectivity, such as RF signals transmitted using directional (i.e.,narrow beam) antennas or infrared signals that may optionally be usedwith focusing optics. Still other implementations are possible usingappropriate interfaces and protocols such as, by way of example and notintended to be limiting, USB 2.0, Firewire, ATA/IDE, RS-232, RS-422,RS-485, 802.11 a/b/g/n, Wi-Fi, Ethernet, IrDA, FDDI (fiber distributeddata interface), token-ring networks, or multiplexing techniques basedon frequency, time, or code division. Some implementations mayoptionally incorporate features such as error checking and correction(ECC) for data integrity, or security measures, such as encryption(e.g., WEP) and password protection.

A number of implementations have been described. Nevertheless, it willbe understood that various modification may be made. For example,advantageous results may be achieved if the steps of the disclosedtechniques were performed in a different sequence, or if components ofthe disclosed systems were combined in a different manner, or if thecomponents were supplemented with other components. Accordingly, otherimplementations are contemplated.

What is claimed is:
 1. A computer program product (CPP) tangiblyembodied in a computer readable medium and containing instructions that,when executed, cause a processor to perform operations to calculate afit-quality metric of a virtual mask to a virtual face, the operationscomprising: receiving facial-elevation data corresponding to a face of aperson; retrieving from data-memory locations mask-mating elevation datacorresponding to a facial-mating surface of a mask model; fitting a chinregion of the received facial-elevation data to a chin-mating surface ofthe retrieved mask-mating elevation data, wherein fitting the chinregion comprises: i) identifying a menton region of the receivedfacial-elevation data; and, ii) aligning a menton region of theretrieved mask-mating elevation data with the identified menton regionof the received facial-elevation data; determining a mask-modelalignment angle at which both the fitted chin region is substantiallymaintained relative to the chin-mating surface and a nose-bridge regionof the received facial-elevation data is fit to a nose-bridge-contactingsurface of the retrieved mask-mating elevation data; translating themask model aligned at the determined angle in a facial direction adistance calculated to correspond with an actual translation of a masksubject to a force of a mask-securing device when securing a mask to aface; and, calculating a fit-quality metric of an interface between thereceived facial-elevation data and the retrieved mask-mating elevationdata when fitted to both the chin region and the nose-bridge region atthe determined alignment angle.
 2. The CPP of claim 1, whereintranslating the mask model aligned at the determined angle comprisescalculating a translation distance corresponding to the predeterminedforce, wherein the calculated translation distance is the distance atwhich an integrated force along the facial mating surface of the maskmodel is approximately equal to the predetermined force threshold. 3.The CPP of claim 1, containing further instructions that, when executed,cause a processor to perform operation comprising: associating anelasticity index to one or more locations of the mask-mating elevationdata, the elasticity index corresponding to a force needed to deform thefacial mating surface.
 4. The CPP of claim 1, containing furtherinstructions that, when executed, cause a processor to perform operationcomprising: retrieving, from data-memory locations, mask-interiorelevation data corresponding to a dead-space region of a mask model. 5.The CPP of claim 4, containing further instructions that, when executed,cause a processor to perform operation comprising: calculating adead-space volume associated with an air space between the retrievedmask-interior elevation data and the received facial-elevation datacircumscribed by the retrieved mask-mating elevation data.
 6. A computerprogram product (CPP) tangibly embodied in a computer readable mediumand containing instructions that, when executed, cause a processor toperform operations to calculate a fit-quality metric of a virtual maskto a virtual face, the operations comprising: receiving facial-elevationdata corresponding to a face of a person; retrieving from data-memorylocations mask-mating elevation data corresponding to a facial-matingsurface of a mask model; fitting a chin region of the receivedfacial-elevation data to a chin-mating surface of the retrievedmask-mating elevation data; determining a mask-model alignment angle atwhich both the fitted chin region is substantially maintained relativeto the chin-mating surface and a nose-bridge region of the receivedfacial-elevation data is fit to a nose-bridge-contacting surface of theretrieved mask-mating elevation data; and, calculating a fit-qualitymetric of an interface between the received facial-elevation data andthe retrieved mask-mating elevation data when fitted to both the chinregion and the nose-bridge region at the determined alignment angle. 7.The CPP of claim 6, wherein fitting a chin region comprises: identifyinga menton region of the received facial-elevation data; and, aligning amenton region of the retrieved mask-mating elevation data with theidentified menton region of the received facial-elevation data.
 8. TheCPP of claim 6, containing further instructions that, when executed,cause a processor to perform operations comprising: translating the maskmodel aligned at the determined angle in a facial direction using apredetermined force corresponding to an actual force of a mask-securingdevice when securing a mask to a face.
 9. The CPP of claim 8, whereintranslating the mask model aligned the determined angle comprisescalculating a translation distance corresponding to the predeterminedforce, wherein the calculated press distance is the translation at whichan integrated force along the facial mating surface of the mask model isapproximately equal to the predetermined force.
 10. The CPP of claim 6,containing further instructions that, when executed, cause a processorto perform operations comprising: identifying a facial-mating datasubset of the received facial-elevation data that is in close proximityto the facial-mating surface of the retrieved mask-mating elevation datawhen fitted to both the chin region and the nose-bridge region at thedetermined alignment angle.
 11. The CPP of claim 6, containing furtherinstructions that, when executed, cause a processor to perform operationcomprising: assigning a hardness index to one or more locations aroundthe identified facial-mating data subset, the hardness indexcorresponding to an expected flesh deformation capability associatedwith a corresponding location of a face of a person.
 12. The CPP ofclaim 7, containing further instructions that, when executed, cause aprocessor to perform operation comprising: retrieving from data-memorylocations mask-interior elevation data corresponding to a dead-spaceregion of a mask model.
 13. The CPP of claim 8, containing furtherinstructions that, when executed, cause a processor to perform operationcomprising: calculating a dead-space volume associated with an air spacebetween the retrieved mask-interior elevation data and the receivedfacial-elevation data circumscribed by the identified facial-mating datasubset.
 14. The CPP of claim 9, wherein calculating a fit-quality metriccomprises selecting a fit-quality metric associated with the calculateddead-space volume.
 15. The CPP of claim 6, wherein receivingfacial-elevation data comprises: receiving, from a three-dimensionalscanner, signals indicative of a facial position and a facial elevation.16. A method for calculating a fit-quality metric of a virtual mask to avirtual face, the method comprising: receiving facial-elevation datacorresponding to a face of a person; retrieving from data-memorylocations mask-mating elevation data corresponding to a facial-matingsurface of a mask model; fitting a chin region of the receivedfacial-elevation data to a chin-mating surface of the retrievedmask-mating elevation data; determining a mask-model alignment angle atwhich both the chin-mating surface is substantially maintained relativeto the fitted chin region and a nose-bridge-contacting surface of theretrieved mask-mating elevation data is fit to a nose-bridge region ofthe received facial-elevation data; and, calculating a fit-qualitymetric of an interface between the received facial-elevation data andthe retrieved mask-mating elevation data 155 when fitted to both thechin region and the nose-bridge region at the determined alignmentangle.
 17. The method of claim 16, wherein fitting a chin regioncomprises: identifying a menton region of the received facial-elevationdata; and, aligning a menton region of the retrieved mask-matingelevation data with the identified menton region of the receivedfacial-elevation data.
 18. The method of claim 16, containing furtherinstructions that, when executed, cause a processor to performoperations comprising: translating the mask model aligned at thedetermined angle in a facial direction using a predetermined forcecorresponding to an actual force of a mask-securing device when securinga mask to a face.
 19. The method of claim 18, wherein translating themask model aligned the determined angle comprises calculating atranslation distance corresponding to the predetermined force, whereinthe calculated translation distance is the distance at which anintegrated force along the facial mating surface of the mask model isapproximately equal to the predetermined force.
 20. The method of claim16, containing further instructions that, when executed, cause aprocessor to perform operations comprising: identifying a facial-matingdata subset of the received facial-elevation data that is in closeproximity to the facial-mating surface of the retrieved mask-matingelevation data when fitted to both the chin region and the nose-bridgeregion at the determined alignment angle.